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Article:Performance Analysis of Various Data Mining Algorithms:A Review

by Dharminder Kumar, Suman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 6
Year of Publication: 2011
Authors: Dharminder Kumar, Suman
10.5120/3906-5476

Dharminder Kumar, Suman . Article:Performance Analysis of Various Data Mining Algorithms:A Review. International Journal of Computer Applications. 32, 6 ( October 2011), 9-16. DOI=10.5120/3906-5476

@article{ 10.5120/3906-5476,
author = { Dharminder Kumar, Suman },
title = { Article:Performance Analysis of Various Data Mining Algorithms:A Review },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 6 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number6/3906-5476/ },
doi = { 10.5120/3906-5476 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:27.361914+05:30
%A Dharminder Kumar
%A Suman
%T Article:Performance Analysis of Various Data Mining Algorithms:A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 6
%P 9-16
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
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 Naïve Bayes ,Clustering algorithms (like k-Means 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.

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Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree Rule set Classifier kNN Naïve Bayes k-Means EM SVM Apriori

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