Abstract: Data mining is a process which finds useful patterns from large amount of data. Data mining is the core part of the Knowledge Discovery in Database (KDD). It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis. Data mining techniques can be classified into summarization, classification, clustering, association rules and trend analysis. Classification aims to discover a small set of rules in the database that forms an accurate classifier. There are different classification methods such as decision tree, Rule Induction, Fuzzy rule , Neural Networks etc., In this paper we are analyzing the performance of 3 classification algorithms namely J48 Decision Tree, Decision Table, RBFNetwork. We use the Yeast datasets for calculating the performance of classification algorithms by using the training set parameter. And finally a comparative analysis based on the performance factors such as the Classification and execution time is performed on all the algorithms.
Keywords: Classification, J48 Decision Tree, Decision Table, RBFNetwork, Yeast dataset.