CFP last date
22 April 2024
Reseach Article

Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets

by Swarndeep Saket J., Sharnil Pandya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 5
Year of Publication: 2016
Authors: Swarndeep Saket J., Sharnil Pandya
10.5120/ijca2016910701

Swarndeep Saket J., Sharnil Pandya . Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets. International Journal of Computer Applications. 146, 5 ( Jul 2016), 19-23. DOI=10.5120/ijca2016910701

@article{ 10.5120/ijca2016910701,
author = { Swarndeep Saket J., Sharnil Pandya },
title = { Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 5 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number5/25394-2016910701/ },
doi = { 10.5120/ijca2016910701 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:33.191529+05:30
%A Swarndeep Saket J.
%A Sharnil Pandya
%T Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 5
%P 19-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering techniques are application tools to analyze stored data in various fields. Clustering is a process to partition meaningful data into useful clusters which can be understood easily and has analytical value. The K-Means and K-Medoid Algorithms in their existing structure carry certain weaknesses. For example in case of K-Means algorithm ‘deformation’ and ‘deviations’ may arise due to the misbehavior and disruption in the computing process. Similarly in case of K-Medoid Algorithm a lot of iteration is required which consumes huge amount of time and their by reduces the efficiency of clustering. In the present paper, we have proposed a new Modified K-Medoid Algorithm for improving efficiency and scalability for the study of large datasets. The extended K-Medoids Algorithm stand better in terms of execution time, quality of clusters, number of clusters and number of records than the comparative results of K-Means and K-Medoid Algorithm. Extended K-Medoid Algorithm is evaluated using sample real employee datasets and results are compared with K-Means and K-Medoids.

References
  1. J. Kleinberg, (2002) “An impossibility theorem for clustering,” in Proc. Conf. Advances in Neural Information Processing Systems, 2002,vol. 15, pp. 463–470.
  2. A. Gordon, C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H. Bock, and Y. Bada, Eds (1998) “Cluster validation,” in Data Science, Classification, and Related Methods. New York: Springer-Verlag, 1998, pp. 22–39
  3. P. Indira Priya and Dr. D.K. Ghosh (2013),” A Survey on Different Clustering Algorithms in Data Mining Technique”, (IJMER) International Journal of Modern Engineering Research, Jan-Feb 2013, Vol. No-3, Issue-1,pp. 267-274.
  4. Pradeep Rai and Shubha Singh (2010), “A Survey of Clustering Techniques”, International Journal of Computer Applications (0975-8887) ,October 2010, Vol. 7-No. 12, pp. 1-5,
  5. Jiawei Han and Micheline Kamber,(2000) “Data Mining Techniques”, Morgan Kaufmann Publishers, 2000.
  6. Shalini S Singh & N C Chauhan,(2011) ,“K-means v/s K-medoids: A Comparative Study”, National Conference on Recent Trends in Engineering & Technology, 2011.
  7. .J. Han, M. Kamber, and M. Kauffman (2006), Data Mining: Concepts and Techniques, 2nd ed., 2006.
  8. Dr. Aishwarya Batra, “Analysis and Approach :K-Means and K-Medoids Data Mining Algorithms”, 5th IEEE International Conference on Advanced Computing & Communications Technologies (ICACCT-2011), ISBN 81-87885-03-3.
  9. Tagaram Soni Madhulatha (2011),"Comparison between K-Means and K-Medoids Clustering Algorithms", Communications in Computer and Information Science, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering k-means k-Medoids