Open Access Open Access  Restricted Access Subscription or Fee Access

Validation of Region-based Crossover for Clustering Problems

Jeevan F. D’Souza, C. Kelly Adams, Andrew Reed

Abstract


The k-means algorithm is a widely used partitional clustering algorithm because of its simplicity and computational efficiency. One problem with the k-means algorithm is that the quality of partitions produced is highly dependent on the initial selection of centers. The problem of center selection has been tackled in the past using genetic algorithms (GA). One of the most effective GA for k-means clustering is the region-based genetic algorithm (RBGA). This research aimed at assessing the RBGA across a variety of cluster representatives and distance metrics. The experimental results show the superior performance of the RBGA, as compared to other popular genetic algorithm approaches, indicating that region-based crossover may prove an effective strategy across a broad range of clustering problems.

Keywords


k-means algorithm, clustering, genetic algorithm, crossover operation, center selection

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.