AN INTEGRATED TECHNIQUE TO ENHANCE THE PERFORMANCE OF THE CLASSIFIERS

ABSTRACT Data Mining is a process by which data can be analyzed, so as to generate useful knowledge. In data mining, Classifiers are the wide accepted effective technique for prediction. It predicts cluster membership for knowledge instances. The goal of classifier is to predict the target class accuracy for each case among the data. Though the classifiers achieve best prediction accuracy in most of cases, fails to achieve the same in few cases particularly in huge dataset. The key idea of the proposed technique is to cluster the data and then apply classification algorithm, there by the performance of classifier is improved. The major datasets used in this experiment are collected from the UCI Machine Learning Repository and a dataset named telecom are collected by a survey. For each dataset it is important to choose a clustering method carefully. Here we used K-means clustering and filtered clustering algorithm using WEKA tool. The performance measures accuracy and ROC are computed and compared to highlight the performance of the model. Based on the experiment it is concluded that the proposed hybrid model performs well than the unitary model. Keywords: Classification, Clustering, K-Means, ROC.