ISSN: 0973-7510

E-ISSN: 2581-690X

K. Vengatesan1 and S. Selvarajan2
1Department of Computer Science and Engineering, Muthayammal Engineering College, Tamilnadu, India.
2Muthayammal College of Engineering, Rasipuram-637408, Tamilnadu, India.
J Pure Appl Microbiol. 2015;9(Spl. Edn. 2):611-618
© The Author(s). 2015
Received: 10/08/2015 | Accepted: 03/09/2015 | Published: 30/11/2015

The clustering is an important task in data mining, the functional analysis of gene clustering investigation can be perfomed by using various algorithms. The clustering techniques are useful to realize gene functions, cellular process, gene regulation and subtypes of cells.  The different techniques are used to measure the performance of the gene overlapping such as CLICK, SOM, and rRFCM. The proposed ErRFCM increase the probability membership of the clusters and also handle the overlapping gene clusters effectively. It is also useful in dealing with probabilistic lower approximation and possibility lower approximation. The proposed methods are used to identify the strong group of Co expressed genes and produce the best result. The gene clusters produced are HCM, FCM, RFCM, SOM, CLICK and rRFCM algorithms, and visualized by Tree View software for 14 Microarray dataset.


Gene Clustering ,Rough fuzzy clustering, Dunn Index, Silhouette Index

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© The Author(s) 2015. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.