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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
Year of Publication: 2016
Swarndeep Saket J., Sharnil Pandya

Swarndeep Saket J. and Sharnil Pandya. Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets. International Journal of Computer Applications 146(5):19-23, July 2016. BibTeX

	author = {Swarndeep Saket J. and 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 = {July 2016},
	volume = {146},
	number = {5},
	month = {Jul},
	year = {2016},
	issn = {0975-8887},
	pages = {19-23},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2016910701},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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Clustering, k-means, k-Medoids