Empirical evaluation of K-Means, Bisecting K-Means, Fuzzy C-Means and Genetic K-Means clustering algorithms

摘要

Clustering is one of the most widely studied problem in machine learning and data mining. The algorithms for clustering depend on the application scenario and data domain. K-Means algorithm is one of the most popular clustering techniques that depend on distance measure. In this work, an extensive empirical evaluation of three significant variations of K-Means algorithm is carried out on the basis of six internal and external validity indices. It has been seen that performance of K-Means and Bisecting K-Means are similar, while Fuzzy C-Means gives better performance and Genetic K-Means performs the best. On the light of empirical result obtained in this paper, method for further improvement of the performance of Genetic K-Means is suggested.