Clustering is the unsupervised learning in which the data is divided into similar groups (cluster) without any prior knowledge. The emerging swarm-based algorithms become an alternative to the conventional clustering methods to enhance the quality of results. Artificial Bee Colony (ABC) Algorithm is one of the Swarm Intelligent based optimization algorithm that exhibit foraging properties of a Honey Bee Swarm. Bacterial Foraging Optimization (BFO) is another Swarm intelligence algorithm which imitates the foraging properties of the E. coli bacteria. In this paper, we hybridize both ABC and BFO by replacing the Scout bee phase of ABC by BFO to have a minimum Intra cluster distance. From the experimental results, it shows the proposed H-ABFO algorithm outplays the traditional K-means, ABC and BFO algorithms.

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Clustering Artificial Bee Colony Algorithm Bacterial Foraging Algorithm And K-means Algorithm.