Research Article
Assessing h- and g-Indices of Scientific Papers using k-Means Clustering
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 100 - Issue 11 |
| Published: August 2014 |
| Authors: S. Govinda Rao, A. Govardhan |
10.5120/17572-8266
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S. Govinda Rao, A. Govardhan . Assessing h- and g-Indices of Scientific Papers using k-Means Clustering. International Journal of Computer Applications. 100, 11 (August 2014), 37-41. DOI=10.5120/17572-8266
@article{ 10.5120/17572-8266,
author = { S. Govinda Rao,A. Govardhan },
title = { Assessing h- and g-Indices of Scientific Papers using k-Means Clustering },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 100 },
number = { 11 },
pages = { 37-41 },
doi = { 10.5120/17572-8266 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A S. Govinda Rao
%A A. Govardhan
%T Assessing h- and g-Indices of Scientific Papers using k-Means Clustering%T
%J International Journal of Computer Applications
%V 100
%N 11
%P 37-41
%R 10.5120/17572-8266
%I Foundation of Computer Science (FCS), NY, USA
Abstract
K-means clustering technique works as a greedy algorithm for partition the n-samples into k-clusters so as to reduce the sum of the squared distances to the centroids. A very familiar task in data analysis is that of grouping a set of objects into subsets such that all elements within a group are more related among them than they are to the others. K-means clustering is a method of grouping items into k groups. In this work, an attempt has been made to study the importance of clustering techniques on h- and g-indices, which are prominent markers of scientific excellence in the fields of publishing papers in various national and international journals. From the analysis, it is evidenced that k-means clustering algorithm has successfully partitioned the set of 18 observations into 3 clusters.
References
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