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Reseach Article

Evolutionary Clustering Technique for finding Significant Solutions

Published on May 2012 by P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare
National Conference on Recent Trends in Computing
Foundation of Computer Science USA
NCRTC - Number 8
May 2012
Authors: P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare
5016d311-b710-461a-bd5b-6605ed9f5acf

P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare . Evolutionary Clustering Technique for finding Significant Solutions. National Conference on Recent Trends in Computing. NCRTC, 8 (May 2012), 19-23.

@article{
author = { P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare },
title = { Evolutionary Clustering Technique for finding Significant Solutions },
journal = { National Conference on Recent Trends in Computing },
issue_date = { May 2012 },
volume = { NCRTC },
number = { 8 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 19-23 },
numpages = 5,
url = { /proceedings/ncrtc/number8/6574-1064/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Trends in Computing
%A P. M. Chaudhari
%A R. V. Dharaskar
%A V. M. Thakare
%T Evolutionary Clustering Technique for finding Significant Solutions
%J National Conference on Recent Trends in Computing
%@ 0975-8887
%V NCRTC
%N 8
%P 19-23
%D 2012
%I International Journal of Computer Applications
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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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Technique Evolutionary Algorithms Reproduction Crossover Mutation Fitness Cluster Validity