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K-Medoids Clustering Technique using Bat Algorithm

Monica Sood, Shilpi Bansal Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/ijais13-450965
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  1. Monica Sood and Shilpi Bansal. Article: K-Medoids Clustering Technique using Bat Algorithm. International Journal of Applied Information Systems 5(8):20-22, June 2013. BibTeX

    @article{key:article,
    	author = "Monica Sood and Shilpi Bansal",
    	title = "Article: K-Medoids Clustering Technique using Bat Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 5,
    	number = 8,
    	pages = "20-22",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Clustering is one of the data analysis methods that are widely used in data mining. In this method, we partitioned the data into different subset which is known as cluster. Cluster analysis is the data reduction toll for classifying a "mountain? of information into manageable meaningful piles. This method is vast research area in the field of data mining. In this paper, a partitioning clustering method that is K-Medoids algorithm is used with Bat algorithm. We proposed a new algorithm based on the echolocation behaviour of bats to know the initial value to overcome the K-Medoids issues. In this algorithm, we can find the initial representative object easily with the help of using Bat algorithm. They provide us better cluster analysis and we can achieve efficiency. This paper introduces the combination of K-Medoids clustering algorithm and Bat Algorithm. In this paper we show the difference between K-Medoid Clustering Technique with Bat Algorithm & K-medoid itself.

Reference

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Keywords

Swarm Intelligence, Bat Algorithm, Clustering, K-Medoids Clustering Technique