Data clustering: review and investigation of parallel genetic algorithms for revealing clusters

Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Ashish Ghosh, Rajib Mall and Gi-Nam Wang

International Journal of Applied Management Science, 2014, vol. 6, issue 3, 212-243

Abstract: This paper review some state-of-the art non-parallel and parallel approaches of data clustering and present the usefulness of parallel genetic algorithms (PGAs) for clustering. In solving the clustering problem many traditional methods stuck in local optimal solutions. Further, in such algorithms the user also asked to provide the number of clusters but in general it is unknown to the user. Therefore, clustering becomes a trial-and-error work and is also very expensive in terms of computation time. Genetic algorithm can be a solution to reduce the local optimal problem but it demands very high computation time, hence in this paper, we are using a PGA for data clustering. PGA not only exploits large search space to find the cluster centres but also reduces the computation vastly. Furthermore, this work exploits both data parallelism by distributing the data being mined across all available processors, and control parallelism by distributing the population of individuals across all available processors. Data parallelism coupled with control parallelism has shown to yield the best parallelism results on a diverse set of benchmark real life datasets taken in this article.

Keywords: data mining; clustering; parallel genetic algorithms; PGAs; data parallelism; control parallelism; data clusters. (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=65205 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injams:v:6:y:2014:i:3:p:212-243

Access Statistics for this article

More articles in International Journal of Applied Management Science from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().