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Performance Analysis of Various Data Mining Algorithms: A Review

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Author(s): Dharminder Kumar | Suman

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 32;
Issue: 6;
Start page: 9;
Date: 2011;
Original page

Keywords: Decision Tree | Rule set | Classifier | kNN | Naive Bayes | k-Means | EM | SVM | Apriori

ABSTRACT
Data warehouse is the essential point of data combination for business intelligence. Now days, there has been emerging trends in database to discover useful patterns and/or correlations among attributes, called data mining. This paper presents the data mining techniques like Classification, Clustering and Associations Analysis which include algorithms of Decision Tree 'like C4.5', Rule set Classifier ,kNN and Naive Bayes ,Clustering algorithms 'like kMeans and EM 'Machine Learning 'Like SVM',Association Analysis'like Apriori'. These algorithms are applied on data warehouse for extracting useful information. All algorithms contain their description, impact and review of algorithm. We also show the comparison between the classifiers by accuracy which shows ruleset classifier have higher accuracy when implement in weka.These algorithms useful in increasing sales and performance of industries like banking, insurance, medical etc and also detect fraud and intrusion for assistance of society.

Tango Jona
Tangokurs Rapperswil-Jona

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