Performance Analysis of Partition and Evolutionary Clustering Methods on Various Cluster Validation Criteria

Abstract

Large quantity of data has been accumulating tremendously due to digitalization. But the accumulated data are not converted into useful patterns. This gap is conquered by using exploratory data analysis techniques. Clustering is one of the vital technologies in exploratory data analysis. It is a methodology to arrange data objects as per their characteristics. Traditional clustering approaches, namely leader, K-means, ISODATA and evolutionary-based approaches like genetic algorithm, particle swarm optimization, social group optimization methods, are also implemented on benchmark data set. Evolutionary-based clustering methods are derived from the existing hard clustering methods for finding optimal results. Performance analysis of the above clustering techniques should be validated through different cluster validation methods. The performance analysis reveals evolutionary clustering methods convergence rate is better than partition clustering methods. ISODATA performs better in various aspects on large data. In this work analyzed performance of hard and evolutionary clustering methods on execution time, internal cluster validity criteria.