Combining validity indexes and multi-objective optimization based clustering

Abstract

In this study, we present a clustering approach that automatically determines the number of clusters before starting the actual clustering process. This is achieved by first running a multi-objective genetic algorithm on a sample of the given dataset to find the set of alternative solutions for a given range. Then, we apply cluster validity indexes to find the most appropriate number of clusters. Finally, we run CURE to do the actual clustering by feeding the determined number of clusters as input. The reported test results demonstrate the applicability and effectiveness of the proposed approach.