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Research Papers On Data Mining 2015

Here is the list of 50 selected papers in Data Mining and Machine Learning. You can download them for your detailed reading and research. Enjoy!


Data Mining and Statistics: What’s the Connection?

Data Mining: Statistics and More?, D. Hand, American Statistician, 52(2):112-118.

Data Mining, G. Weiss and B. Davison, in Handbook of Technology Management, John Wiley and Sons, expected 2010.

From Data Mining to Knowledge Discovery in Databases, U. Fayyad, G. Piatesky-Shapiro & P. Smyth, AI Magazine, 17(3):37-54, Fall 1996.

Mining Business Databases, Communications of the ACM, 39(11): 42-48.

10 Challenging Problems in Data Mining Research, Q. Yiang and X. Wu, International Journal of Information Technology & Decision Making, Vol. 5, No. 4, 2006, 597-604.

The Long Tail, by Anderson, C., Wired magazine.

AOL’s Disturbing Glimpse Into Users’ Lives, by McCullagh, D., News.com, August 9, 2006

General Data Mining Methods and Algorithms

Top 10 Algorithms in Data Mining, X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. motoda, G.J. MClachlan, A. Ng, B. Liu, P.S. Yu, Z. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, Knowl Inf Syst (2008) 141-37.

Induction of Decision Trees, R. Quinlan, Machine Learning, 1(1):81-106, 1986.

Web and Link Mining

The Pagerank Citation Ranking: Bringing Order to the Web, L. Page, S. Brin, R. Motwani, T. Winograd, Technical Report, Stanford University, 1999.

The Structure and Function of Complex Networks, M. E. J. Newman, SIAM Review, 2003, 45, 167-256.

Link Mining: A New Data Mining Challenge, L. Getoor, SIGKDD Explorations, 2003, 5(1), 84-89.

Link Mining: A Survey, L. Getoor, SIGKDD Explorations, 2005, 7(2), 3-12.
Semi-supervised Learning

Semi-Supervised Learning Literature Survey, X. Zhu, Computer Sciences TR 1530, University of Wisconsin — Madison.

Introduction to Semi-Supervised Learning, in Semi-Supervised Learning (Chapter 1) O. Chapelle, B. Scholkopf, A. Zien (eds.), MIT Press, 2006. (Fordham’s library has online access to the entire text)

Learning with Labeled and Unlabeled Data, M. Seeger, University of Edinburgh (unpublished), 2002.

Person Identification in Webcam Images: An Application of Semi-Supervised Learning, M. Balcan, A. Blum, P. Choi, J. lafferty, B. Pantano, M. Rwebangira, X. Zhu, Proceedings of the 22nd ICML Workshop on Learning with Partially Classified Training Data, 2005.

Learning from Labeled and Unlabeled Data: An Empirical Study across Techniques and Domains, N. Chawla, G. Karakoulas, Journal of Artificial Intelligence Research, 23:331-366, 2005.

Text Classification from Labeled and Unlabeled Documents using EM, K. Nigam, A. McCallum, S. Thrun, T. Mitchell, Machine Learning, 39, 103-134, 2000.

Self-taught Learning: Transfer Learning from Unlabeled Data, R. Raina, A. Battle, H. Lee, B. Packer, A. Ng, in Proceedings of the 24th International Conference on Machine Learning, 2007.

An iterative algorithm for extending learners to a semisupervised setting, M. Culp, G. Michailidis, 2007 Joint Statistical Meetings (JSM), 2007
Partially-Supervised Learning / Learning with Uncertain Class Labels

Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers, V. Sheng, F. Provost, P. Ipeirotis, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.

Logistic Regression for Partial Labels, in 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Volume III, pp. 1935-1941, 2002.

Classification with Partial labels, N. Nguyen, R. Caruana, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.

Imprecise and Uncertain Labelling: A Solution based on Mixture Model and Belief Functions, E. Come, 2008 (powerpoint slides).

Induction of Decision Trees from Partially Classified Data Using Belief Functions, M. Bjanger, Norweigen University of Science and Technology, 2000.

Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth, P. Smyth, M. Burl, U. Fayyad, P. Perona, KDD Workshop 1994, AAAI Technical Report WS-94-03, pp. 109-120, 1994.

Recommender Systems

Trust No One: Evaluating Trust-based Filtering for Recommenders, J. O’Donovan and B. Smyth, In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), 2005, 1663-1665.

Trust in Recommender Systems, J. O’Donovan and B. Symyth, In Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI-05), 2005, 167-174.

General resources available on this topic:

ICML 2003 Workshop: Learning from Imbalanced Data Sets II

AAAI ’2000 Workshop on Learning from Imbalanced Data Sets


A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, G. Batista, R. Prati, and M. Monard, SIGKDD Explorations, 6(1):20-29, 2004.

Class Imbalance versus Small Disjuncts, T. Jo and N. Japkowicz, SIGKDD Explorations, 6(1): 40-49, 2004.

Extreme Re-balancing for SVMs: a Case Study, B. Raskutti and A. Kowalczyk, SIGKDD Explorations, 6(1):60-69, 2004.

A Multiple Resampling Method for Learning from Imbalanced Data Sets, A. Estabrooks, T. Jo, and N. Japkowicz, in Computational Intelligence, 20(1), 2004.

SMOTE: Synthetic Minority Over-sampling Technique, N. Chawla, K. Boyer, L. Hall, and W. Kegelmeyer, Journal of Articifial Intelligence Research, 16:321-357.

Generative Oversampling for Mining Imbalanced Datasets, A. Liu, J. Ghosh, and C. Martin, Third International Conference on Data Mining (DMIN-07), 66-72.

Learning from Little: Comparison of Classifiers Given Little of Classifiers given Little Training, G. Forman and I. Cohen, in 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, 161-172, 2004.

Issues in Mining Imbalanced Data Sets – A Review Paper, S. Visa and A. Ralescu, in Proceedings of the Sixteen Midwest Artificial Intelligence and Cognitive Science Conference, pp. 67-73, 2005.

Wrapper-based Computation and Evaluation of Sampling Methods for Imbalanced Datasets, N. Chawla, L. Hall, and A. Joshi, in Proceedings of the 1st International Workshop on Utility-based Data Mining, 24-33, 2005.

C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling, C. Drummond and R. Holte, in ICML Workshop onLearning from Imbalanced Datasets II, 2003.

C4.5 and Imbalanced Data sets: Investigating the effect of sampling method, probabilistic estimate, and decision tree structure, N. Chawla, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.

Class Imbalances: Are we Focusing on the Right Issue?, N. Japkowicz, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.

Learning when Data Sets are Imbalanced and When Costs are Unequal and Unknown, M. Maloof, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.

Uncertainty Sampling Methods for One-class Classifiers, P. Juszcak and R. Duin, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.

Active Learning

Improving Generalization with Active Learning, D Cohn, L. Atlas, and R. Ladner, Machine Learning 15(2), 201-221, May 1994.

On Active Learning for Data Acquisition, Z. Zheng and B. Padmanabhan, In Proc. of IEEE Intl. Conf. on Data Mining, 2002.

Active Sampling for Class Probability Estimation and Ranking, M. Saar-Tsechansky and F. Provost, Machine Learning 54:2 2004, 153-178.

The Learning-Curve Sampling Method Applied to Model-Based Clustering, C. Meek, B. Thiesson, and D. Heckerman, Journal of Machine Learning Research 2:397-418, 2002.

Active Sampling for Feature Selection, S. Veeramachaneni and P. Avesani, Third IEEE Conference on Data Mining, 2003.

Heterogeneous Uncertainty Sampling for Supervised Learning, D. Lewis and J. Catlett, In Proceedings of the 11th International Conference on Machine Learning, 148-156, 1994.

Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction, G. Weiss and F. Provost, Journal of Artificial Intelligence Research, 19:315-354, 2003.

Active Learning using Adaptive Resampling, KDD 2000, 91-98.

Cost-Sensitive Learning

Types of Cost in Inductive Concept Learning, P. Turney, In Proceedings Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning.

Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection, P. Chan and S. Stolfo, KDD 1998.


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data mining research papers 2015 IEEE PAPER

LogCluster-A Data Clustering and Pattern Mining Algorithm for Event Logs
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Abstract:Modern IT systems often produce large volumes of event logs, and event pattern discovery is an important log management task. For this purpose, data mining methods have been suggested in many previous works. In this paper, we present the LogCluster

A Survey of Sequence Patterns in Data Mining Techniques
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Abstract Data mining techniques are used in many areas in the world to retrieve the useful knowledge from the very large amount of data. Sequence pattern mining is the important techniques in data mining concepts with the wide range of applications. The applications

Information and Communication Technology (ICT) As a Characteristic Tool for Development of Tribes: A Study by Using Data Mining
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ABSTRACT Information and Communication Technologies (ICTs) have potential to play a vital role in social development. Several projects have attempted to adopt these technologies to improve the reach, enhance the coverage base by minimizing the

Application of big data in education data mining and learning analytics A lterature review
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Abstract The usage of learning management systems in education has been increasing in the last few years. Students have started using mobile phones, primarily smart phones that have become a part of their daily life, to access online content. Student's online activities

An Analytical Study of Data Mining with the Help of UML Model and its Application
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Abstractata Mining analytical study goes for modeling and database Design methods. In this Research Paper we discuss Knowledge discovery methods and modeling of data mining tables. When we create models in data mining for UML Models and its application we use

A Brief Review of Alternative Uses of Data Mining: Education, Engineering, Others
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Abstract Data mining is the process of finding hidden information in large amounts of data. The most common previous uses of data mining have been to help businesses to gain and maintain a competitive advantage as well as to answer questions, solve problems, or

An Analysis of Misclassification Error Detection in Mails Using Data Mining Techniques
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Abstract:his research is to classify the filtered data. The main purpose of this research is to reduce the error rate of the data and to improve the accuracy. In the previous techniques of classification there may be some misclassification. But in this research the problem of

These proceedings contain the papers accepted to the 1st ECML/PKDD Workshop on Sta-tistically Sound Data Mining, which took place at the French Institute for
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The aim of the workshop was to strengthen collaboration between data mining and statistical researchers, by providing a forum to debate problems faced by both communities, including statistical soundness of data mining methods, scalability issues for standard statistical

Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media
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Abstract Foodborne illness afflicts 48 million people annually in the US alone. Over 128,000 are hospitalized and 3,000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the

Is Efficacy of Milnacipran in Fibromyalgia Predictable A Data Mining Analysis of Baseline and Outcome Variables.
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Abstract Objectives: Minalcipran has been approved for the treatment of fibromyalgia in several countries including Australia. Australian's agency considered that the overall efficacy is moderate, although clinically significant, and could be translated into a real and strong

Expert System to Predict the Type of Fever Using Data Mining Techniques on Medical Databases
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finding the most important medical symptoms and laboratory data helps in building an expert system to predict the dengue fever in early stages. We developed in this project a new

Impact of Known Input Output Attacks in Euclidean Distance Preserving Perturbation for Privacy Preserving Data Mining
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Abstract: rivacy preserving Data Mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In this technique, some statistical data that is to be released, so that it can

Comparative Study of Frequent Item Set in Data Mining
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ABSTRACT In this paper, we are an overview of already presents frequent item set mining algorithms. In these days frequent item set mining algorithm is very popular but in the frequent item set mining computationally expensive task. Here we described different

Dataset Fingerprints: Exploring Image Collections Through Data Mining
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Abstract As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content. In this paper, we propose dataset fingerprints, a new and powerful method based

Design and Implementation of Content Based Image Retrieval Using Data Mining and Image Processing Techniques
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Abstract: Now a days people are interested in using digital images. There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color, texture and size are three important

Spatial Patterns of the Indications of Acupoints Using Data Mining in Classic Medical Text: A Possible Visualization of the Meridian System
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The indications of acupoints are thought to be highly associated with the lines of the meridian systems. The present study used data mining methods to analyze the characteristics of the indications of each acupoint and to visualize the relationships

Learning Analytics on Coursera Event Data: A Process Mining Approach
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Abstract. Massive Open Online Courses (MOOCs) provide means to offer learning material in a highly scalable and flexible manner. Learning Analytics (LA) tools can be used to understand a MOOC's effectiveness and suggest appropriate intervention measures. A

Lumping versus splitting: the need for biological data mining in precision medicine
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Biological data mining is playing an increasingly important role throughout the spectrum of biological and biomedical research with broad implications for the understanding of life science questions such as the tree of life and practical applications of such knowledge to

Classification Algorithms and Comparison in Data Mining
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ABSTRACT In present days, tons of data and information exist for each and everyone, Data can now be kept in many various kinds of databases as well as information repositories, besides being available online or in hard copy. With such big amount of data, a need for

Discovery of Acupoints and Combinations with Potential to Treat Vascular Dementia: A Data Mining Analysis
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The prevalence of vascular dementia (VaD) is high among the elderly. Acupuncture, a popular therapeutic method in China, can improve memory, orientation, calculation, and self- managing ability in VaD patients. However, in clinical acupuncture and acupuncture

KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
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Data Mining (DM) is the process for automatic discovery of high level knowledge by obtaining information from real world, large and complex data sets [26], and is the core step of a broader process, called Knowledge Discovery from Databases (KDD). In addition to

Cognition of Parameters' Role on Vertical Control Device for Aerodynamic Characteristics of Aircraft Using Data Mining
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Abstract The new concept to place the vertical airfoil device as control surface has been discovered so as to improve the aerodynamic performance of aircraft. The concept was predicated on not only the several devices as vortex generator and winglet but also the

Data Mining with Decision Tree to Evaluate the Pattern on Effectiveness of Treatment for Pulmonary Tuberculosis: A Clustering and Classification Techniques
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Abstract-Data mining is a process which helps in uncovering interesting data patterns in large volume of data. This procedure has become an ever more activity in all areas of medical science research especially in healthcare circumstances. Data mining has The threat of climate change makes it crucial to improve our understanding of the climate system. However, the volume and diversity of climate data from satellites, environmental sensors, and climate models can make the use of traditional analysis tools impractical and

Learning in Competitive Environments through Observation: A Data Mining Approach
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Abstract: In this study, we investigated the problem of learning about other agents in a competitive environment. For our application domain, we used a two-player Snake Game. We modeled the agent learner by observing the actions of a benchmark agent, a hard

Tuning, Diagnostics Data Preparation for Generalized Linear Models Supervised Algorithm inData Mining Technologies
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Abstract-Data mining techniques are the result of a long process of research and product development. Large amount of data are searched by the practice of Data Mining to find out the trends and patterns that go beyond simple analysis. For segmentation of data and also

Enhancing the Performance of BitApriori Algorithm in Data Mining using an Effective DataStructure
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Abstract Exploration frequent item sets within transaction databases, time-series databases, and far different form of databases has been analyzed popularly within information exploration investigation. An exploration frequent merchandise unit is one of the nearly all

Data-mining Based Detection of Glaciers: Quantifying the Extent of Alpine Valley Glaciation
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Abstract: The extent of glaciation in alpine valleys often gives clues to past climates, plate movement, mountain landforms, bedrock geology and more. However, without field investigation, the degree to which a valley was affected by a glacier has been difficult to

Data Mining in Healthcare for Heart Diseases
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ABSTRACT: Data Mining is the area of research which means digging of useful information or knowledge from previous data. There are different techniques used for the data mining. Data mining may used in different fields including Healthcare. Heart or Cardiovascular

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Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming

Data Mining Methods for Case-Based Reasoning in Health Sciences
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Abstract. Case-based reasoning (CBR) systems often refer to diverse data mining functionalities and algorithms. This article locates examples, many from health sciences domains, mapping data mining functionalities to CBR tasks and steps, such as case

Use of Data Mining Techniques to Improve the Effectiveness of Sales and Marketing
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Abstract: Classification and patterns extraction from customer data is very important factors for business support and decision making. Identification of newly emerging trends is needed in business process. Sales patterns from inventory data indicate market trends and can be

A global immune deficit in Alzheimer's disease and mild cognitive impairment disclosed by a novel data mining process
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Abstract. Alzheimer's disease (AD) is the most common form of dementia, while mild cognitive impairment (MCI) causes a slight but measurable decline in cognitive abilities. A person with MCI has an increased risk of developing AD or another dementia. Thus, it is of Data preprocessing is an often neglected but major step in the data mining process. The data collection is usually a process loosely controlled, resulting in out of range values, eg, impossible data combinations (eg, Gender: Male; Pregnant: Yes), missing values, etc.

Techniques for Web Mining of Various Forms of Existence of Data on Web: A Review
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Abstract: Information extraction is known to be the task of extracting knowledge from any meaningful text automatically. It provides services to the users who retrieve the information by firing query on Internet. When huge amount of information is extracted from large

Data mining for the internet of things: literature review and challenges
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The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view,

Application of Data Mining based on Classifier in Class Label Prediction of Coal Mining Data
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Abstract For issue that coal mining data tends to be incomplete, noisy and inconsistent, some popular classifiers are applied to predict class label of coal mining dataset. Noise and bad points are rejected from coal mining data which will be exchanged to input format

The Analysis of Physical Effects on Presence in the 4D Attraction Film using the Data Mining
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Abstract A 4D attraction film is an entertainment presentation system combining a 3D film with physical effects that provide other experience in senses. A wide variety of devices that the audience can be immersed in the story should be reflected in the contents production

Incorporation Stuck Between Customer Relationship Management (CRM) and DataWarehousing for Data Mining Application
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Abstract The focus of CRM investment is to automate the sales force ie, to build separate applications for CRM having their owndatabases. These are just like operational systems, from where the data has to move to data warehouses later on. But now thetrend is

Accelerating data mining on incomplete datasets by bitmaps-based missing value imputation
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Abstract:Among all 'big data'research issues, the veracity challenge, which refers to the precision and accuracy of the data, has not received as much attention. Traditionally, it has been well known that problems related to data quality, such as incomplete, redundant,

Data Mining in Genomics Proteomics
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With the development of high-throughput, next-generation sequencing and other advanced technologies, a large number of gene expression profiles have been produced. Many of these profiles are available from public databases [1-3]. A challenging research problem that

Trajectory Data Mining in Distributed Sensor Networks
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The prevalence of distributed sensor networks on the Internet has allowed the field to become a relevant and active research area, attracting professionals and researchers from a variety of fields and disciplines. By incorporating trajectory information, we can bring

Homo Sapiens: Epidermal Growth Factor Receptor (DNA Data Mining)
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Abstract Epidermal growth factor plays an important role in the Regulation of the cell growth, proliferation and differentiation by binding to its receptor EGFR. IT has a receptor called EGFR it leads to the rapid internalization and releases the Lysozyme. It reduces the cell

Data Mining Techniques in DoS/DDoS Attack Detection: A Literature Review
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Abstract:This paper attempts to classify papers concerning DoS/DDoS attack detection using data mining techniques. 35 papers were selected and carefully reviewed by authors from two online journal databases. Each of selected paper was classified based on the

Implementation of Data Mining Algorithm to Analysis Breast Cancer
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Abstract The aim is to evaluate the analytical data to see if the domain can be a subject to data mining. The next step is to evaluate several data mining methods with respect to their applicability to the given data. This is to show which of the techniques are particularly

Introduction to Data Mining with CART
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Page 1. Introduction to DataMining with CART Dan Steinberg Mykhaylo Golovnya The Final Triumph Rising interest in datamining over the past decade Availability of huge data sets requiring analysis Need to automate or accelerate and improve analysis process

Data Outsourcing based on Secure Association Rule Mining Processes
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Abstract Data mining is the process of extracting information from data warehousing applications. Data outsourcing is the major task in present days, for accessing services and other features of the database processing. But sometimes this process may achieve to

A Study on Information Retrieval and Extraction for Text Data Words using Data MiningClassifier
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Abstract:Text mining studies are gaining more importance recently because of the availability of the increasing number of the electronic documents from a variety of sources. In the current scenario, text classification gains lot of significance in processing and retrieval

Corporate Default Prediction with Data Mining Techniques
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ABSTRACT Default has recently upraised as an excessive concern due to the recent world crisis. Early forecasting of firms default provides decision-support information for financial and regulatory institutions. In spite of several progressive methods that have widely been

Efficient Clustering of Web Documents Using Hybrid Approach in Data Mining
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Abstract-There is a huge amount of data present on internet. The procedure to find important information on the web can be very hectic. Using today's search engines it is hard to go through the big number of returned urls and documents. Hence there is a need to arrange

Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach
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Abstract A key challenge to evaluate data-mining bias in stock return anomalies is that we do not observe all the variables considered by researchers. We overcome this challenge by constructing a universe of fundamental signals from financial statements and by using a

In vivo data mining and in silico metabolic profiling to predict diverse hepatotoxic phenotypes: Case study of piperonyl butoxide
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Background Short-term (or lower dosage): Mild changes, including liver steatosis and enlargement (hepatocyte hypertrophy) observed in dogs [1]. Our previous research proposed the glycol side chain of PBO to be responsible for the prosteatogenic mode of

A Literature Survey on Contrast Data Mining
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Abstract: Data mining refers to extracting or mining knowledge from large amount of data. Data mining also called KDD (Knowledge Discovery). There are some types of classification techniques: Decision Tree, Na�ve Bayesian, Neural Network, Contrast Data Mining.

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ABSTRACT Data mining is rapid growth field in order to mine the information from the huge data set. Data mining techniques are results of long process of research and product development and include artificial neural networks, decision trees and genetic algorithms.

Measuring Information Quality for Privacy Preserving Data Mining
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Abstract:In the strive for knowledge discovery in a world of ever-growing data collection, it is important that even if a dataset is altered to preserve people's privacy, the information in the dataset retains as much quality as possible. In this context,quality refers to the After ten years since PAKDD 2005 in Ha Noi, PAKDD was held again in Vietnam, during May 19�22, 2015, in Ho Chi Minh City. PAKDD 2015 is the 19th edition of the Pacific-Asia Conference series on Knowledge Discovery and Data Mining, a leading international

Application of Data Mining Techniques for Predicting CD4 Status of Patients on ART in Jimma and Bonga Hospitals, Ethiopia
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Abstract Background: Many of the reports on HIV/AIDS shows that the number of ART registered patients are increasing from time to time. Despite those reports show increasing of patients' number, they did not try to make prediction of attributes based on the given

Human Detection in a Surveillance Video using Data Mining Techniques-A Survey
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Abstract:Face Detection is extracting facial features from face images and then authenticating identity according to its features. Due to a variety of potential applications in information security, law enforcement, access control and human computer interaction, it

Securitization of Mortgage Backed Securities using Data Mining Techniques
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Abstract This paper describes different data mining techniques used in securitization of Mortgage Backed Securities. Mortgage Default Risk Analysis is used in many financial institutes for accurate analysis of consumer data to find defaulter and valid customer. For Abstract The recent applications of data mining such as biological, scientific, financial and others are changing data regularly, which is uncertain and incomplete. For finding tendency in these data up-to-date, we need to modify existing data mining algorithms with dynamic

Decision Tree C4. 5 algorithm and its enhanced approach for Educational Data Mining
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Abstract-Data mining tools for educational research issues are prominently developed and used in many countries. Decision Tree is the most widely applied supervised classification data mining technique. The learning and classification steps of decision tree induction are

Proposing an approach to calculate headway intervals to improve bus fleet scheduling using a data mining algorithm
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Abstract The growth of AVL (Automatic Vehicle Location) systems leads to huge amount of data about different parts of bus fleet (buses, stations, passenger, etc.) which is very useful to improve bus fleet efficiency. In addition, by processing fleet and passengers' historical

Data Mining using Fuzzy Methods
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Abstract-Data mining is the focal venture in a procedure called learning revelation in databases, to be specific the step in which displaying strategies are connected. A few exploration territories like measurements, computerized reasoning, machine learning, and

Route Pattern Mining From Personal Trajectory Data
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The discovery of route patterns from trajectory data generated by moving objects is an essential problem for location-aware computing. However, the high degree of uncertainty of personal trajectory data significantly disturbs the existing route pattern mining approaches

Cloud Computing Based Forensic Analysis for Mobile Applications Using Data Mining
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Abstract: With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win+ Intel alliance in PC, Android+ ARM alliance dominates in Mobile Internet, the apps replace the PC client The 2nd International Conference on Computational Intelligence in Data Mining (ICCIDM- 2015) is organized by RIT, Berhampur, Odisha, India on 5 and 6 December 2015. ICCIDM is an international forum for representation of research and developments in the fields of

A New Technique to Optimize User's Browsing Session using Data Mining
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Abstract:From a wide range of options, an ambiguous query would return different search results for different users when they submit it to a search engine. However, ambiguous query/topic submitted to search engine doesn't satisfy user information needs every time,

Natural Customer Ranking of Banks in Terms of Credit Risk by Using Data Mining: A Case Study: Branches of Mellat Bank of Iran
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Abstract ith the development of trade and business throughout the world, needs to become more widespread financial dealings that led to the development of the business activities of banks and new banks were established. Banking and financial systems, credit risk

Mining Frequent and Correlated Items Using Map Reduce Framework and Tree DataStructure
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Abstract:Big Data has a characteristics like large like volume, velocity, variability, complexity value. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. Fr The combinatorial explosion of FIM methods

Review on Heart Disease Prediction System using Data Mining Techniques
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Abstract Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Dat a mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. Data mining tools can answer

Linkage Pattern Mining Method for Multiple Sequential Data with Noise.
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Abstract:Linkage pattern mining is a data mining technique that finds frequent patterns that appear repeatedly across multiple sequential data. This technique does not assume similarity or correlation between the frequent patterns in a linkage pattern; thus, it is

Weather Forecasting and Climate Changing Using Data Mining Application
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Abstract: Weather forecasting is an important application in meteorology and has been one of the most scientifically and technologically challenging problems around the world. In this paper, we investigate the use of data mining techniques in forecasting maximum

Various Data Mining Techniques used to Study Student's Academic Performance
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Abstract: To extract decision from huge amount of data is tedious work. And for this now days various data mining tools are available in market which are focused of data mining techniques to find pattern recognition and able to support decision making process using

How to Address the Problems of Data Mining in Chemistry: The Application of Artificial Neural Networks
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Abstract Recent years, data mining has become a very popular concept in computer science. Its application has covered many crucial subjects. However, the systemic research based on relevant techniques in the field of chemistry is still non-existent. Here, we This volume contains 19 research papers belonging, roughly speaking, to the areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors' contributions to honour and celebrate

Data mining based on statistical analysis of Chinese Traditional Setting-up exercise on healthy undergraduate students.
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Abstract Objective: To investigate the effects of Chinese Traditional Setting-up Exercise on blood glucose, heart rate and vital capacity of healthy undergraduates Methods: 200 undergraduate volunteers were selected according to the diagnostic standards and

Practical Approaches: A Survey on Data Mining Practical Tools
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Abstract: To Build Powerful world, Data Analysis play very important role. The progress and application of data mining algorithms requires the use software tools. As the number of available tools continues to grow, the choice of the most suitable tool becomes

Data Mining technique and its Application: Association rules mining for Korean language
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Abstract. This paper explains the importance of Association Rules Mining for of Korean language (text). Association rules mining can also be used for mining association rules from textual data with some modifications. Which can then, help for generating statistical

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ABSTRACT Conventional spatial queries such as vary search and nearest neighbor retrieval, involve solely conditions on objects 'geometric properties. Today, several trendy applications call for novel varieties of queries that aim to seek out objects satisfying

System Design, Investigation and Countermeasure of Phishing Attacks using Data MiningClassification Methods and its Analysis
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Abstract The phishing is a kind of e-commerce lure which is intended to steal the confidential information of the internet user by making identical website of legitimate one in which the contents and images most likely remains similar to the legitimate website. The other way

Analysis of Extreme Precipitation Events Using a Novel Data Mining Approach
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Abstract An innovative data mining approach is presented and applied to investigate the climatic causes of extreme climatic events. Our approach comprises two main steps of knowledge extraction, applied successively in order to reduce the complexity of the

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Abstract-The purpose data mining is to extract the useful information from a bulky data set. Clustering analysis is an important technique in the field of data mining. It is the process of grouping similar vectors of a document into number of clusters. The basic procedure for

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ABSTRACT In this paper, we apply different data mining approaches for the purpose of examining and predicting students' dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students

Prediction of Traffic Flow Combination Model Based on Data Mining
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Abstract It is an important to quickly and accurately forecasting road network traffic flow in intelligent transportation systems, Aiming at the forecasting problem of short-term traffic flow, this paper proposed a traffic flow prediction algorithm, which based on traffic flow

Parallel Data Mining Architecture for Big Data
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Abstract:Big Data is concerned with the large-volume, complex, growing data sets with multiple and autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all domains of

Cluster Analysis of E-Commerce Sites with Data Mining Approach
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Abstract With the rapid development of E-Commerce, how to evaluate the E-Commerce sites accurately has become an important issue. However, to cluster E-Commerce sites correctly and accurately is not an easy thing based on characteristics of high dimensions and

Analytical Study of Data Mining Techniques for Software Quality Assurance
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Abstract:Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the

Ultrametricity of dissimilarity spaces and its significance for data mining
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Abstract. We introduce a measure of ultrametricity for dissimilarity spaces and examine transformations of dissimilarities that impact this measure. Then, we study the influence of ultrametricity on the behavior of two classes of data mining algorithms (kNN classification

The Prediction of Students' Academic Performance Using Classification Data MiningTechniques
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Abstract Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students' data which can be used to discover valuable pattern pertaining students'

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Abstract-Data Mining refers to the mining of useful and interesting patterns from large data sets. Medical data now a day is available in abundance but without proper mining they cannot be used. The goal of data mining is to turn data that are facts, numbers, or text

Comparative Study of Popular Classification Techniques of Data Mining
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Abstract: Data mining is a term related to the extraction of the unknown or hidden information which is previously unknown from the huge database. In data mining process various patterns are extracted and this is why it is also known as pattern discover. There are a

Talent Explorer Using Data Mining
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Abstract Our proposed application,�Talent explorer using data mining is based on educational data mining and can be operated using client-server architecture. It would take into account the previous performances of a student and predict whether a student is

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Abstract This Technical Note looks at data mining both on individual SME accounts and on the SME portfolio as a whole, and suggests how and why extraction and review of data should be undertaken. At a time when banks are starting to offer SME banking services

Analysis of Employment Data Mining for University Student based on Weka Platform
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Abstract: This paper took the historical data of university graduates employment and the employment guidance as the study object and purposes, tried to find out the useful information hidden in the historical data of employment through the data mining of the

Image Retrieval Using Data Mining and Image Processing Techniques
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Abstract: In the domain of Image processing, Image mining is advancement in the field of data mining. Image mining is the extraction of hidden data, association of image data and additional pattern which are quite not clearly visible in image. It's an interrelated field that

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ABSTRACT In this paper, we analyzed the real estate transaction data, and built prediction models for the real estate price using data mining algorithms, and validate the models. The datasets we used were publicly available from King County in the State of Washington-the

Discovering flood rising pattern in hydrological time series data mining during the pre monsoon period
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In India, the months from January to May comes under the Pre-Monsoon season. The country is influenced by two seasons of rains, accompanied by seasonal reversal of winds from January to July. During the winters, dry and cold air blowing from the northerly

The Research of Data Mining Classification Algorithm that Based on SJEP.
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Abstract Data Mining is dedicated to the analysis and understanding of data, revealing hidden data inside knowledge of technology. Classified as an important research topic in data mining, in recent years, data mining sector presents a new knowledge model, called

An Approach Based on Data Mining to Support Management in E-Commerce
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ABSTRACT The ability of managers to analyze large volumes of data is not enough to identify all relevant associations and necessary for the decision-making process. Make use of a classification model can generate information that typically a manager could not

A Review on Tuberculosis Using Data mining Approaches
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Abstract-Tuberculosis is one of the prominent diseases for all persons in established countries as well as India. It is the utmost communal reason of demise in human being. The high occurrence of Tuberculosis has augmented expressively in previous years. In

Data Mining Algorithms Application in Diabetes Diseases Diagnosis: A Case Study
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Abstract: Suitable diagnosis and selection of appropriate ways of treatment for people who are afflicted with diabetes are of great importance since ignorance in remedying diabetes can cause other organs to be defected and also can lead to death. Nowadays, there are

Improvements in Data Mining Association Rules Algorithm.
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Abstract Because of the traditional Apriori algorithm in data mining in the process of operation for a long time, and produce a large number of unrelated item sets, caused great waste of data space, so this article puts forward an improved Apriori algorithm based on

Crime vs. demographic factors revisited: Application of data mining methods
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Abstract The aim of this article is to inquire about correlations between criminal phenomena and demographic factors. This international-level comparative study used a dataset covering 56 countries and 28 attributes. The data were processed with the Self-Organizing Map (

Outlier Reduction using Hybrid Approach in Data Mining
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Abstract:The Outlier detection is very active area of research in data mining where outlier is a mismatched data in dataset with respect to the other available data. In existing approaches the outlier detection done only on numeric dataset. For outlier detection if we use

A Comparison of Data Mining Techniques for Anomaly Detection in Relational Databases
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Abstract:Data mining has gained a lot of attention in recent years especially with the advent of big data. In line with this, relational database management systems (RDBMS) have also become the ultimate layer in preventing malicious data access. However,

A Collection of Sport Activity Files for Data Analysis and Data Mining
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The increased popularity of smartphones and watches allows athletes to train smarter. More and more cyclists today use a different sports applications, that run on mobile devices, such as Strava, Endomondo, Garmin Connect and many others. Primary tasks of such The great interest that emerges from the use of data mining techniques has caused that there was a large amount of data mining books and papers available in literature. The majority of techniques or methodologies that are available for use are published and can

Role of Educational Data Mining Model in university course selection by the students.
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There are various definitions of database in different contexts by the previous researchers. According to Matheus et al (1991), data mining is a process to discover unobvious but useful information. Curt (1995) defined data mining as a process of transforming vocabulary of

Research of Massive Spatiotemporal Data Mining Technology Based on Cloud Computing
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Abstract: This paper mainly discusses developing a new mass data analysis system in face of the above challenges, based on cloud computing technology, cloud service provided by Amazon and HBase. Through the research of the EC2, S3 and EMR service provided by

Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application AwareData Mining
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Because the static cloud platform cannot satisfy the diversity of mobile Internet service and inefficient data mining problems, we presented a reliable and efficient data mining cloud platform construction scheme based on the mobile Internet service opportunistic driving

Educational Data Mining: Performance Evaluation of Decision Tree and Clustering Techniques Using WEKA Platform
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Abstract Data Mining plays a vital role in information management technology. It is a computational process of finding patterns from large databases. It mainly focuses on extracting knowledge from the given or the available data. Different knowledge extracting

The Predictive Modeling of Old-age High Blood Pressure in Korea using Data-mining
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Abstract. This study developed old-age high blood pressure prediction model by using epidemiological data and, based on the developed model, provides empirical data which can be used in health care programs and education in local community. Data were from

Data Mining Techniques For Secured Data Sharing And Privacy Preserving Apply Data Mining Approach On Web Service Using AES Algorithm
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ABSTRACT In this paper, the enhancing data techniques are used in the user database for secure their database from other unauthorized user. This technique is useful for privacy preserving; securely share data among N number of parties. And also apply data mining

Analytical Study of Data Mining on Data Stream Using Skewed Distribution of Data
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Abstract:Analytical Study of Data Mining is an important data analysis tool that uses a model from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a

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ABSTRACT This research proposes an efficient model for predicting the survival rate of patients affected by lung cancer. The researchers collected data from four feature categories (population, recognition, treatment, and result) of cancer patients based on the importance

An Analysis and Review on Various Techniques of Mining Big Data
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Big Data is described as large-volume, heterogeneous, autonomous sources with distributed and decentralized control, and seeks to explore complex and evolving relationships among data [1]. These characteristics make it an extreme challenge for

An effort to select a preferable metaheuristic model for knowledge discovery in data mining
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Abstract: Swarm intelligence computation is becoming an important topic in the field of decision making, and is successfully applied in many fields, which is indicating a fairly great potential for development. Recently, much research work has been carried out by swarm

Secure Multi-party Communication in Data-mining Applications
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Abstract Data mining extracts knowledge or patterns from a large amount of data. Secure communication is an issue of shared database applications. Fundamentally, secure multi party computation is to enable a number of networked parties to carry out distributed

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Abstract-Data Mining has emerged as a technique of extracting and discovering new knowledge in data implicit in a large data warehouse so as to enable better decisions and strategy formulation. It is an effective and rapid process of data searching, retrieval and

A Study on Mining Lung Cancer Data for Increasing or Decreasing Disease Prediction Value by Using Ant Colony Optimization Techniques
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Cancer is the most Important Cause of death for both men and women. Lung Cancer is the Uncontrolled growth of abnormal cells that start off in one or both Lung Usually in

A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments
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This study suggests a methodology called a smart ubiquitous data mining (UDM) that consolidates homogeneous models in a smart ubiquitous computing environment. It tests the suggested model with financial datasets. It basically induces rules from the dataset

Mining Frequent Spatio-Temporal Items in Trajectory Data
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Abstract The time aspect is not currently taken into account for finding a region of interesting (ROI) or a hot region, so that due to the time to visit frequently a place cannot be determined, it is difficult to discover the visiting regularity for a moving object. To this end, the spatio-

Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data
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Abstract: This paper presents an approach to automatically mine rules in time series data representing physiological parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for each physiological time series data. The

A User-Intelligent Adaptive Learning Model For Learning Management System Using Data Mining And Artificial Intelligence
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Abstract Entire world is revolving towards digital space as a result of the Internet and other emerging web technologies are helping the society to reach the universe. ICT and e- learning are growing radically fast and have captured a major role in higher educational

Mining Himachal Pradesh State Electricity Board for ATC Losses with Data MiningAssociation Rules
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Abstract: The leading business challenges faced by the technical world today in business field are rightful extraction of information from huge data bases and its application into future ventures to minimise the losses, maximize the profits, and multiply the dividends. In this

Research on Patent Information Analyzing and Predicting System Based on Data Mining
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Abstract Patents, as technological innovation achievements and sign, are potential and intangible assets of enterprises and can enhance their core competitiveness. How to obtain useful information and knowledge from a large number of patents data, have become an

A Study on Data Mining Techniques and Genetic Algorithm in Education Sector
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Abstract:This paper is examine the analysis of students' and prediction of students' performance. Data mining techniques are playing vital roles in Higher education institution. This paper is reviewing some data mining techniques like association rules, classification

Implementing Behavior Prediction using Frequent Pattern Tree using Data Mining Technique in Smart Home
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Abstract A fully fledged smart home system contains a wide variety of household appliances. With the popularity of smart home, production of large amount of data in our everyday life occurs. These existing data is used to analyze users' behavior and how to

Integration of Data Mining Systems using Sequence Process
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Abstract-In this paper we discuss about the large database has been a major concern in research community, due to difficulty of analyzing huge numbers of data using only OLAP tools. This process implies lots of computational power, memory space, which can only be

Survey of Data Mining Techniques in Healthcare
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Abstract: Data Mining is one of the most leading areas of research that is helpful in healthcare organization. Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining plays an important

A Purpose of Data Mining in Banking Sector
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Abstract: Banking systems collect huge amounts of data on day to day basis, be it customer information, transaction details, risk profiles, credit card details, credit limit and collateral details, compliance and Anti Money Laundering (AML) related information, trade finance

Comparative Analysis of Classification Techniques in Data Mining Using Different Datasets
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Abstract: Data mining is the invention of knowledge and useful information from the large amounts of data stored in databases. It is referred as an analysis study of the Knowledge discovery in database process or KDD. Data mining tools are used in forecasting future

A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation
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Traumatic brain injury (TBI) is a critical public health and socioeconomic problem throughout the world. Cognitive rehabilitation (CR) has become the treatment of choice for cognitive impairments after TBI. It consists of hierarchically organized tasks that require repetitive

A Comparative Study on Data Mining Tools
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ABSTRACT A research needs to collect data from various sources and analysis those data with some techniques for predict or decision making process. After collections of various data, main task it to maintain data apply transformation and preprocessing of large data

Using Microsoft Dynamics CRM for Analytical CRM: A Curriculum Package for Business Intelligence or Data Mining Courses
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Abstract: The purpose of this curriculum package attempts to show how to use the Microsoft SQL Server Business Intelligence Development Studio to access Microsoft Dynamics CRM and conduct a data mining and analysis. First, students use the Dynamics CRM 2013 to

Data Integration and Mining based on Web Big Data
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Abstract As the revolution of Web 2.0 technology, more and more novel service industries, such as social network, web of things and mobile internet emerge. The data of Web explosive growth is called the big data, which is hottest. Because of the great value of big

Informative Review on Data Partition and Node Stability in Mining
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Abstract: The web information available is vast and expanding to meet the current challenges of intelligent techniques. There are billions of pages in the web, where pages huge in quantity is not the problem, its disorganized state leads the user to face the

A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques
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Abstract Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects. Software quality metrics are a subset of software metrics that focus on the quality aspects

A Comparative Study of Selected Classification Algorithms of Data Mining
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Abstract:Data Mining is the process of extracting hidden knowledge from large volumes of raw data. Data mining has been defined as the nontrivial extraction of previously unknown, implicit and potentially useful information from data. Classification techniques are being

A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases using Data Mining Methods
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Abstract: Background: To date, the analysis of the implications of dietary patterns on lifestyle diseases is based on data coming either from clinical studies or food surveys, both comprised of a limited number of participants. This article demonstrates that linking big

Data Mining in Functional Test Content Optimization
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Abstract:This paper reviews the data mining methodologies [1]-[4] proposed for functional test content optimization where tests are sequences of instructions or transactions. Basic machine learning concepts and the key ideas of these methodologies are explained.

The Study on Relationship Verification of e-wom and Hotel Operation Performance by Data Mining Technique
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Abstract The power of e-word-of-mouth are impact Hotel industry dramatically in the recent years. The Ambassador Hotel Group is a five-star rated business hotel type in Taiwan. Quantitative and qualitative data are retrieved from internet respectively based on data

A Hybrid Approach to improve the Anomaly Detection Rate Using Data Mining Techniques
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Abstract An Intrusion Detection System is a device or software application that monitors events occurring on the network and analyzes it for any kind of malicious activity that violates computer security policy. With an increase in dependency rate on the internet, there is a

Trends in Multi-Relational Data Mining Methods
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ABSTRACT Multi-Relational Data Mining (MRDM) methods search for patterns that involve multiple tables (relations) from a relational database. MRDM aims to discover knowledge directly from relational data. Multi-Relational classification aims to build a classification

Synthesis of Two Solutions of Mobility Prediction Based on Data Mining Techniques
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Abstract:In this paper, we present a synthesis of two solutions for mobility prediction using data mining techniques such as classification and clustering. Our solution can be implemented in a third generation network by exploiting user information (age, function,

A Fuzzy C-mean based Data Mining Algorithm Used in the Bioinformation
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Abstract Data mining technology is a powerful tool to solve the problem. It is widely used to identify potentially useful information. Fuzzy principle based fuzzy C-means is a modification of commonly used C-means clustering technique. In the paper, a modified fuzzy C-means

Data Mining Methods for Rule Designing and Rule Triggering in Active Database Systems
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Abstract Active database system has been introduced to extend the database functionality. It is capable of detecting specific events and automatically reacting to them by executing certain actions either inside or outside the database. This behavior is usually specified

Data Mining with Python (Working draft)
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Python has grown to become one of the central languages in data mining offering both a targeted numerical computations. This book is continuously being written and grew out of course given at the Technical

Application of Data Mining in Personalized Remote Distance Education Web System
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Abstract: This paper uses web log mining technology for different users to take a different service policy and provide different services and conduct individualized services. Also the paper uses basic theory application of web data mining to remote education process in

A Comparative Study and Literature Survey on Privacy Preserving Data Mining Techniques
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Abstract-Privacy preserving data mining is concerned mainly about the hiding of the sensitive information and is a well-researched branch even today. In our daily life, the information sharing is more intuitive but it requires a secure protection so that the

Performance Analysis and Comparison of Various Data Mining Techniques
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Abstract Data Mining is a powerful tool for academic intervention. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used

Mining Social Media Data For Understading Students Learning Experience
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Abstract Students' informal conversations on social media (eg Twitter, Facebook) shed light into their educational experiences opinions, feelings, and concerns about the learning process. Data from such un-instrumented environments can provide valuable knowledge

To Study Artificial Neural Networks in Data Mining and Its Method
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Abstract: Companies have been collecting data for decades, building massive data warehouses in which to store it. Even though this data is available, very few companies have been able to realize the actual value stored in it. The question these companies are

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Abstract:Sequence Mining is one of the most commonly used technique in data mining. Sequence mining is the process of mining frequent patterns from a large datasets. The exiting algorithms have some limitations in predicting frequent patterns, in terms of time,

Future Trends of Data Mining in Predicting the Various Diseases in Medical Healthcare System
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Abstract The thriving medical applications of data mining in the fields of medicine and public health has led to the popularity of its use in knowledge discovery in databases (KDD). Data mining has revealed novel biomedical and healthcare acquaintances for clinical decision

Opinion Mining on Social Media: Based on Unstructured Data
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Abstract:Opinion Mining, also known as sentiment analysis, refers to the identification and extraction of subjective information from the source materials. Recently, microblogging has been a very popular tool for communication among Internet users. It is well known that

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Abstract:-There are 20 Greatest Engineering Achievements of this 20th century. The class is to decide on what they believe the top three achievements are. Each group in the class is assigned to one of three tiers whose jobs are to determine the top singlet, pair, and triplet

Emerging Trends in Associative Classification Data Mining
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Abstract:Utilising association rule discovery to learn classifiers in data mining is known as Associative Classification (AC). In the last decade, AC algorithms proved to be effective in devising high accurate classification systems from various types of supervised data sets.

Data Mining Algorithm Based on Fuzzy Neural Network
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Abstract: In this paper, the fuzzy neural network is selected as the algorithm for data mining (DM), introducing the artificial neural network into the fuzzy logic by treating it as a computing tool, it is a networklized description form by using the artificial neural network

A review on security in Data Mining
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Abstract:Privacy is one of the most important properties of an information system must satisfy, in which systems the need to share information among different, not trusted entities, the protection of sensible information has a relevant role. Thus privacy is becoming an

Graph-based Educational Data Mining (G-EDM 2015)
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Fundamentally, a graph is a simple concept. At a basic level a graph is a set of relationships {e (n0, n2), e (n0, nj),, e (nj- 1, nj)} between elements. This simple concept, however, has afforded the development of a complex theory of graphs [1] and rich algorithms for

Data mining and machine learning in e-Science Central using Weka
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This paper describes the integration and use of elements of the Weka open source machine learning toolkit within the cloud based data analytics e-Science Central Platform. The purpose of this is to extend the data mining capabilities of the e- Science Central platform using

Data Mining Technique A Critical Survey (Knowledge Discovery from Data)
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Abstract Data mining technique can be defined as it is the activity to extract the some information from the large data base the aim is to discover the hidden information or actual relationship of the data to the metadata. Today we find application in wide range of business, medical

Integrating Normalization with Random Tree Data Mining Approach for Mining Cloud Based Big Data's of Asthma Patient's
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Abstract The potential of cloud computing for overriding the needs for deploying various infrastructures for running a server based services brought up a revolutionary change in the way the traditional demands of the people use to be handled. Cloud computing provides

Increasing the Efficiency of CRM Process Using Data Mining Practices
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Abstract: In recent times the customer relationship management (CRM) has been achieved an increasing fame in business administration. CRM comprises all the steps which an organization utilizes to create and establish valuable relationships with the customers/

Exploitation of safety measures in Data Mining
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Abstract In this paper we have paying attention a variety of techniques, approaches and different areas of the research which are helpful and marked as the significant field of data mining Technologies. As we are aware that many MNC's and large organization are

Study of different Data Mining system Platform
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As we know what is importance of data mining system for big and large organization, when the company is working in different parts of country and producing large amount of data then it very important to analyze that data which account for productivity and cost benefits from

D16. 1 First Report on Data Mining
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Executive Summary ARIADNE, the Advanced Research Infrastructure for Archaeological Dataset Networking in Europe, will facilitate a central web portal that provides access to archaeological data from various sources in a standardized and open format. This format

Dynamic Cost-sensitive Ensemble Classification based on Extreme Learning Machine forMining Imbalanced Massive Data Streams
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Abstract In order to lower the classification cost and improve the performance of the classifier, this paper proposes the approach of the dynamic cost-sensitive ensemble classification based on extreme learning machine for imbalanced massive data streams (

Determination of the Effect of Somatic Cell Count on Udder Measurements and Subclinical Mastitis with Data Mining Method
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One of the diseases widely seen in modern dairy farms and that caused huge economic losses is mastitis (Kaya et al., 2011). The more common form of mastitis is subclinical mastitis (Park et al., 1982). The subclinical mastitis can be diagnosed by observing the

A Comprehensive Study of Educational Data Mining
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Abstract: Educational data mining is a new discipline in research community that applies various tools and techniques of data mining (DM) to explore data in the field of education. This discipline helps to learn and develop models for the growth of education environment

A Survey based Research for Data Mining Techniques to Forecast Water Demand in Irrigation
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Abstract Data mining is an emerging technology in the recent decade. There are several application areas in which data mining is used widely. It is also useful in agriculture for various purposes. In this paper our focus is on the various techniques used to predict/

Personalization of student in course management systems on the basis using method of data mining
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ABSTRACT Individualization of learning through ICT allows to students not only the possibility choose the time and place to study, but especially pace adoption of new knowledge on the basis of preferred learning styles. Analysis of learning processes

A Review of Feature Selection Algorithms for Data Mining Techniques
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Abstract Feature selection is a pre-processing step, used to improve the mining performance by reducing data dimensionality. Even though there exists a number of feature selection algorithms, still it is an active research area in data mining, machine learning and pattern

Text Data Mining with Different Comparisons
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Abstract:Text data mining should be useful for anticipating new technologies and new uses for existing technologies, insofar as one can attempt to connect complementary pieces of information across two different domains, or subsets, of the scientific literature. The

Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining
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Abstract: The security of the large database that contains certain crucial information, it will become a serious issue when sharing data to the network against unauthorized access. Privacy preserving data mining is a new research trend in privacy data for data mining and

Cloud Computing Environments Parallel Data Mining Policy Research
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Abstract With the rapid development of computer science and technology, more and more data is stored in the computer storage media, data mining (DM) emerged as an


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