Author(s): Roohollah Etemadi | Alireza Hajieskandar
Journal: Indian Journal of Computer Science and Engineering
ISSN 0976-5166
Volume: 2;
Issue: 6;
Start page: 902;
Date: 2011;
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Keywords: Data mining | K-means Clustering Algorithm | Genetic Algorithm
ABSTRACT
K-means clustering algorithm is one of the main algorithms applying in machine learning and pattern recognition. However, as the center of clusters are selected randomly and also due to the dependence ofclustering result on the initial centers of clusters we may trap into local optima centers. In this paper a new genetic algorithm approach based on k-means algorithm is suggested in which the centers of clusters are selected better and in an appropriate manner. In order to increase the efficiency of this algorithm, in each stage, the layout of cluster centers which are in the form of chromosomes are changed with respect to the best chromosome. By estimation of results of the proposed approach on a standard data set and also comparison ofthis algorithm with other related algorithms we can show that our approach is more efficient than k-means algorithm and other algorithms which have been selected in this paper for comparison purposes.
Journal: Indian Journal of Computer Science and Engineering
ISSN 0976-5166
Volume: 2;
Issue: 6;
Start page: 902;
Date: 2011;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Data mining | K-means Clustering Algorithm | Genetic Algorithm
ABSTRACT
K-means clustering algorithm is one of the main algorithms applying in machine learning and pattern recognition. However, as the center of clusters are selected randomly and also due to the dependence ofclustering result on the initial centers of clusters we may trap into local optima centers. In this paper a new genetic algorithm approach based on k-means algorithm is suggested in which the centers of clusters are selected better and in an appropriate manner. In order to increase the efficiency of this algorithm, in each stage, the layout of cluster centers which are in the form of chromosomes are changed with respect to the best chromosome. By estimation of results of the proposed approach on a standard data set and also comparison ofthis algorithm with other related algorithms we can show that our approach is more efficient than k-means algorithm and other algorithms which have been selected in this paper for comparison purposes.
