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Entropy Based Mean Clustering: An Enhanced Clustering Approach | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Entropy Based Mean Clustering: An Enhanced Clustering Approach

V.V. Jaya RamaKrishnaiah1*, K. Ramchand H Rao2 and R. Satya Prasad3

1A.S.N. Degree College, Tenali, AP, India

2A.S.N. Women’s Engineering College, Nelapadu, AP, India

3Acharya Nagarjuna University, Guntur, AP, India

*Corresponding Author:
V.V Jaya RamaKrishnaiah
A.S.N. Degree College
Tenali, AP, India
E-mail: [email protected]

Received Date: May 08, 2012; Accepted Date: May 25, 2012; Published Date: June 07, 2012

Citation: Jaya RamaKrishnaiah VV, Ramchand H Rao K, Satya Prasad R (2012) Entropy Based Mean Clustering: An Enhanced Clustering Approach. J Comput Sci Syst Biol 5:062-067. doi:10.4172/jcsb.1000091

Copyright: © 2012 Jaya RamaKrishnaiah VV, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Many applications of clustering require the use of normalized data, such as text data or mass spectra mining data. The K –Means Clustering Algorithm is one of the most widely used clustering algorithm which works on greedy approach. Major problems with the traditional K mean clustering is generation of empty clusters and more computations required to make the group of clusters. To overcome this problem we proposed an Algorithm namely Entropy Based Means Clustering Algorithm. The proposed Algorithm produces normalized cluster centers, hence highly useful for text data or massive data. The proposed algorithm shows better performance when compared with traditional K Mean Clustering Algorithm in mining data in terms of reducing time, seed predications and avoiding Empty Clusters.


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