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http://dx.doi.org/10.5391/IJFIS.2008.8.1.001

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)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.8, no.1, 2008 , pp. 1-5 More about this Journal
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.
Keywords
Data Mining; Supervised Learning; Classification Algorithms;
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