A two-step artificial bee colony algorithm for clustering

In the field of data analysis, clustering is a powerful technique which groups the data into different subsets using a distance function. Data belonging to the same subset are similar in nature and offer heterogeneity to the data that reside in other subsets. Clustering has proved its potentiality in various fields such as bioinformatics, pattern recognition, image processing and many more. In this paper, a two-step artificial bee colony (ABC) algorithm is proposed for efficient data clustering. In two-step ABC algorithm, the initial positions of food sources are identified using the K-means algorithm instead of random initialization. Along this, to discover the promising search areas, an improved solution search equation based on social behavior of PSO is applied in the onlooker bee phase of ABC algorithm and abandoned food source location is found by using Hooke and Jeeves-based direct search method. Five benchmark and two artificial datasets are applied to validate the proposed modifications in the ABC algorithm, and results of this study are compared with other well-known clustering algorithms. Both the experimental and statistical analyses show that improvements in ABC algorithm have an advantage over the conventional ABC algorithm for solving clustering problems.