Research Article
Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 146 - Issue 5 |
| Published: Jul 2016 |
| Authors: Swarndeep Saket J., Sharnil Pandya |
10.5120/ijca2016910701
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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 },
year = { 2016 },
volume = { 146 },
number = { 5 },
pages = { 19-23 },
doi = { 10.5120/ijca2016910701 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Swarndeep Saket J.
%A Sharnil Pandya
%T Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets%T
%J International Journal of Computer Applications
%V 146
%N 5
%P 19-23
%R 10.5120/ijca2016910701
%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
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