Performance Comparison of Machine Learning Algorithms on Integration of Clustering and Classification Techniques

Abstract: The abundance of data in business, research, industry, science and in many fields makes it very difficult to handle them. It is complicated to explore any valuable information, needed to take any important decision, but problem is how to discover this precious information. The effective solution may be data mining, which is a very popular topic at present research. Two main techniques of data mining are clustering and classification, which are basically studied as individual approach till now. In this paper we integrated both (clustering and classification) techniques. After combine application of most frequently used clustering (k-means) algorithm with classification (J48, Multilayer Perceptron, BayesNet, NavieBayes) algorithms, the results were compared and the WEKA data mining tool was used