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Performance Analysis of Artificial Fish Swarm based Clustering for Gene Expression Data

IJCA Proceedings on National Conference on lnnovation in Computing and Communication Technology
© 2016 by IJCA Journal
NCICCT 2016 - Number 1
Year of Publication: 2016
M. Raja
H. Hannah Inbarani
M. Thangarasu

M Raja, Hannah H Inbarani and M.thangarasu. Article: Performance Analysis of Artificial Fish Swarm based Clustering for Gene Expression Data. IJCA Proceedings on National Conference on lnnovation in Computing and Communication Technology NCICCT 2016(1):10-15, September 2016. Full text available. BibTeX

	author = {M. Raja and H. Hannah Inbarani and M.thangarasu},
	title = {Article: Performance Analysis of Artificial Fish Swarm based Clustering for Gene Expression Data},
	journal = {IJCA Proceedings on National Conference on lnnovation in Computing and Communication Technology},
	year = {2016},
	volume = {NCICCT 2016},
	number = {1},
	pages = {10-15},
	month = {September},
	note = {Full text available}


The K-Means algorithm is the widely used clustering technique. The performance ofthe K-Means algorithm depends highly on original cluster centers and converges to local minima. This paper proposes hybrid Artificial Fish Swarm Means (AFSK-Means) based clustering algorithm, by combining Particle Swarm Optimization with K-Means (PSOK) and Artificial Fish Swarm Algorithm based K-Means (AFSA). The basic idea is to search around the global solution by AFSK-Means and to increase the information exchange among genes. The effectiveness of the clustering algorithm depends on finding optimal clusters. The Clustering result shows the improved performance of hybrid clustering algorithm AFSK-Means in finding the best solution compared with the algorithms K-Means and PSOK-Means.


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