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Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets
| 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
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
- J. Kleinberg, (2002) “An impossibility theorem for clustering,” in Proc. Conf. Advances in Neural Information Processing Systems, 2002,vol. 15, pp. 463–470.
- 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
- 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.
- 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,
- Jiawei Han and Micheline Kamber,(2000) “Data Mining Techniques”, Morgan Kaufmann Publishers, 2000.
- 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.
- .J. Han, M. Kamber, and M. Kauffman (2006), Data Mining: Concepts and Techniques, 2nd ed., 2006.
- 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.
- Tagaram Soni Madhulatha (2011),"Comparison between K-Means and K-Medoids Clustering Algorithms", Communications in Computer and Information Science, 2011.
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