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Number 8

K-Medoids Clustering Technique using Bat Algorithm

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  • International Journal of Applied Information Systems
  • Foundation of Computer Science (FCS), NY, USA
  • Volume 5 - Number 8
  • Year of Publication: 2012
  • Authors: Monica Sood, Shilpi Bansal
  • 10.5120/ijais13-450965
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  • Monica Sood and Shilpi Bansal 2013. K-Medoids Clustering Technique using Bat Algorithm. International Journal of Applied Information Systems. 5, 8 (June 2013), 20-22. DOI=http://dx.doi.org/10.5120/ijais450965 
  • @article{10.5120/ijais2017451568,
author = {Monica Sood and Shilpi Bansal},
title = {K-Medoids Clustering Technique using Bat Algorithm},
journal = {International Journal of Applied Information Systems},
issue_date = {June 2013},
volume = {5},
number = {},
month = {June},
year = {2013},
issn = {},
pages = {20-22},
numpages = {},
url = {/archives/volume5/number8/470-0965},
doi = { 10.5120/ijais13-450965},
publisher = { xA9 2012 by IJAIS Journal},
address = {}
}

  • %1 450965
%A Monica Sood
%A Shilpi Bansal
%T K-Medoids Clustering Technique using Bat Algorithm
%J International Journal of Applied Information Systems
%@ 
%V 5
%N 
%P 20-22
%D 2013
%I  xA9 2012 by IJAIS Journal

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.

References

  1. G. Komarasamy, Amitabh Vahi: A Optimized K-Means Clustering Technique using Bat Algorithm, Bannari Amman Institute of Technology (2012)
  2. Xin-She Yang: A new metaheuristic Bat inspired Algorithm. Studies in Computational Intelligence, Springer (2010) ;loki
  3. Xin-She Yang: Bat Algorithm for Multi- Objective Optimization, Department of Engineering, University of Cambridge (2011)
  4. Raghuvira Pratap A, K survarna Vani, J Rama Devi, Dr. K Nageswara Rao: An Efficient Density based Improved KMedoids Clustering Algorithm, IJACSA (International Journal of Advanced Computer Science and Application), (2011)(Raghuvira Pratap A, 2011)
  5. Shalini S Singh, NC Chauhan: K-Means v/s K-Medoids: A Comparative Study, National Conference on recent trends in Engineering And Technology (Shalini Singh,2011)
  6. Peng Jin, Yun-Long-Zhu, Kun-Yuan-Hu: A Clustering Algorithm for Data Mining Based on Swarm Intelligence, Shenyang Institute of Automation of the Chinese Academy of Sciences, International Conference, china (2007)
  7. Yue-jiao Gong, Rui-tian XU and Jun Zhang: A Clustering-based Adaptive Parameter Control Method for Continuous Ant Colony Optimization, Department of Computer Science, China (2009)

Keywords

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

Index Terms

Computer Science
Information Sciences