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Comparative Analysis on Classification Data Mining Techniques through WEKA

Lukeshkumar Barapatre, Anand Sharma, Hemant Barapatre


Data mining refers to extraction or mining of information/knowledge from huge amounts of data. Data mining is also called as Knowledge Discovery from Database (KDD). There are number of data mining techniques such as classification and regression; the mining of frequent patterns, associations, and correlations; clustering analysis; and outlier analysis. [1]

Classification is a major technique in data mining which is widely used in various fields.

Classification can be defined as the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the function/model to guess the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (i.e., data objects whose class label is known). Classification models predict categorical class labels. Classification may also called as supervised Learning.[2]

In this paper, we are going to discuss the various techniques of classification.Different  kinds of classification techniques  includes if-then rule, decision tree, Neural Network .Bayesian networks, k-nearest neighbor classifier, and  support vector machine(SVM)[3], and the aim of this study is to provide a comprehensive review of different classification techniques in data mining.


Classification, Data Mining, Decision Tree, Supervised Learning, WEKA, Pattern Evaluation, Neural Network, KDD

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