International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Comparison Study of Classification Algorithms in Data Mining

  • Lee, Seung-Joo (Department of Bioinformatics & Statistics, Cheongju University) ;
  • Jun, Sung-Rae (Department of Bioinformatics & Statistics, Cheongju University)
  • Published : 2008.03.01
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Abstract

Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.

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