EMPIRICAL COMPARISON OF VARIOUS CLUSTERING ALGORITHMS AND METHODS TO DETERMINE OPTIMAL CLUSTERS FOR REAL DATASETS

Abstract

In data mining, Clustering is one of the most powerful unsupervised learning technique to find the similar characteristics among the dataset and to separate dissimilar objects in different groups. As there are various number of clustering algorithms, and every clustering algorithm exhibits different results according to the conditions, the choice of selecting a suitable algorithm and suitable measure for evaluation depends on the clustering objectives and task. Hence the quality of clustering process is determined by the purity of the cluster, cluster analysis plays a important role.