Ccgdc: A New Crossover Operator for Genetic Data Clustering

Volume 11, Issue 3, pp 191-208 http://dx.doi.org/10.22436/jmcs.011.03.03

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Authors

Gholam Hasan Mohebpour - Department of Computer Science, Payame Noor University, Tehran, Iran. Arash Ghorbannia Delavar - Department of Computer Science, Payame Noor University, Tehran, Iran.

Abstract

Genetic algorithm is an evolutionary algorithm and has been used to solve many problems such as data clustering. Most of genetic data clustering algorithms just have introduced new fitness function to improve the accuracy of algorithm in evaluation of generated chromosomes. Crossover operator is the backbone of the genetic algorithm and should create better offspring and increase the fitness of population with maintaining the genetic diversity. A good crossover should result in feasible offspring chromosomes when we crossover feasible parent chromosomes. In this paper we introduce a new crossover operator for genetic data clustering. Experimental results show that clustered crossover for genetic data clustering (CCGDC) creates better offspring and increases the fitness of population and also will not produce illegal chromosome.

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Keywords

• Data mining
• data clustering
• genetic algorithm
• crossover operator
• partitioning

MSC

•  68T05
•  68T01

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