Optimal cluster analysis using hybrid K-Means and Ant LionOptimizer

Abstract

K-Means is a popular cluster analysis method which aims to partition a number of data points into K clusters. It has beensuccessfully applied to a number of problems. However, the efficiency of K-Means depends on its initialization of cluster centers.Different swarm intelligence techniques are applied to clustering problem for enhancing the performance. In this work a hybridclustering approach based on K-means and Ant Lion Optimization has been considered for optimal cluster analysis. Ant LionOptimization (ALO) is a stochastic global optimization model. The performance of the proposed algorithm is compared against theperformance of K-Means, KMeans-PSO, KMeans-FA, DBSCAN and Revised DBSCAN clustering methods based on differentperformance metrics. Experimentation is performed on eight datasets, for which the statistical analysis is carried out. The obtainedresults indicate that the hybrid of K-Means and Ant Lion Optimization method performs preferably better than the other threealgorithms in terms of sum of intracluster distances and F-measure.