International Journal of Computer Applications

© 2014 by IJCA Journal

Volume 100 - Number 11

Year of Publication: 2014

10.5120/17572-8266 |

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

@article{key:article, 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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