A Comparative Analysis of Enhanced Artificial Bee Colony Algorithms for Data Clustering

Clustering aims at the unsupervised learning of objects in different groups. The algorithms, such as K-means and Fuzzy C- Means (FCM) are traditionally used for clustering purpose. Recently, most of the researches and study are concentrated on optimization of clustering process using different optimization methods. The commonly used optimizing algorithms such as Particle swarm optimization, Ant Colony Algorithm and Genetic Algorithms have given some significant contributions for optimizing the clustering results. In this paper, we have proposed two new approaches by enhancing the traditional Artificial Bee Colony (ABC) algorithm, the first approach uses ABC algorithm with K means operator and second approach uses ABC algorithm with FCM operator for optimizing the clustering process. The comparative study of the proposed approaches with existing algorithms in the literature using the datasets from UCI Machine learning repository is satisfactory.