An Effective Method for Clustering Using an Algorithm Stimulated By the Decision Making Processes of Bee Swarms
M. Nandhini1, K. Mohana Prasad2
1M. Nandhini, Computer Science and Engineering, Sathyabama University, Chennai, India.
2K.Mohana Prasad, Computer Science and Engineering, Sathyabama University, Chennai, India.
Manuscript received on January 25, 2014. | Revised Manuscript received on February 13, 2014. | Manuscript published on February 28, 2014. | PP: 240-241  | Volume-3, Issue-3, February 2014. | Retrieval Number:  C2673023314/2013©BEIESP

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Abstract: Cluster is the task of grouping a set of objects into a different cluster. Objects in a cluster are more similar when compared with those object in the other in the cluster in some sense or the other Applications of clustering in biomedical research include gene expression data analysis, genomic sequence analysis, biomedical document mining and MRI image analysis . In data mining, clustering is used for scalability, interpretability and usability, insensitive to order of input records and high dimensionality. In this paper we proposed Correlative Artificial Colony where we consider more relationship between the employ bees and onlookers and extend the exploitation capacity of the ABC algorithm. The result shows that the proposed algorithm reduces computation time, increases the exploitation ability of ABC and produces a good solution in a limited amount of time.
Keywords: Artificial bee colony, Clustering, Correlative artificial bee colony.