CBGDC: A new genetic center based data clustering algorithm based on K-means
Publish place: International Journal of Mechatronics, Electrical and Computer Technology، Vol: 4، Issue: 13
Publish Year: 1393
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJMEC-4-13_022
تاریخ نمایه سازی: 16 فروردین 1395
Abstract:
In this paper, a Center Based Genetic Data Clustering (CBGDC) algorithm based on K-means is proposed. This algorithm is able to detect arbitrary shape clusters and will not converge to local optima. In proposed algorithm a new population initialization method and reinsertion way have been used. Crossover and mutation operators will not be done with a fix probability and a new fitness function based on Silhouette index will be used toevaluate fitness of chromosomes faster. The efficiency of CBGDC has been compared with original genetic data clustering and K-means algorithm on artificial and real life datasets and experimental results show that the CBGDC will decrease clustering error more than original genetic data clustering and K-means.
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Authors
Arash Ghorbannia Delavar
Department of Computer Science, Payame Noor University, PO BOX 19395-3697, Tehran, Iran
Gholam Hasan Mohebpour
Department of Computer Science, Payame Noor University, PO BOX 19395-3697, Tehran, Iran