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Assessing h- and g-Indices of Scientific Papers using k-Means Clustering

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 100 - Number 11
Year of Publication: 2014
S. Govinda Rao
A. Govardhan

Govinda S Rao and A Govardhan. Article: Assessing h- and g-Indices of Scientific Papers using k-Means Clustering. International Journal of Computer Applications 100(11):37-41, August 2014. Full text available. BibTeX

	author = {S. Govinda Rao and A. Govardhan},
	title = {Article: 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},
	month = {August},
	note = {Full text available}


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.


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