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Evolutionary Clustering Technique for finding Significant Solutions

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IJCA Proceedings on National Conference on Recent Trends in Computing
© 2012 by IJCA Journal
NCRTC - Number 8
Year of Publication: 2012
Authors:
P. M. Chaudhari
R. V. Dharaskar
V. M. Thakare

P M Chaudhari, R V Dharaskar and V M Thakare. Article: Evolutionary Clustering Technique for finding Significant Solutions. IJCA Proceedings on National Conference on Recent Trends in Computing NCRTC(8):19-23, May 2012. Full text available. BibTeX

@article{key:article,
	author = {P. M. Chaudhari and R. V. Dharaskar and V. M. Thakare},
	title = {Article: Evolutionary Clustering Technique for finding Significant Solutions},
	journal = {IJCA Proceedings on National Conference on Recent Trends in Computing},
	year = {2012},
	volume = {NCRTC},
	number = {8},
	pages = {19-23},
	month = {May},
	note = {Full text available}
}

Abstract

Evolutionary clustering technique is proposed that opts for cluster centers straight way from the data set, further making it to speed up the fitness evaluation by estimating a data table in advance. It saves the distances among pairs of data points, and by using binary instead of string representation to encode a variable number of cluster centers. The development of ECT has capability to properly cluster different data sets. The experimental results show that the ECT provides a more stable clustering performance in terms of number of clusters and clustering results. These results require less computational time as compared to other GA-based clustering algorithms.

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