Genetic algorithm-based clustering ensemble: determination number of clusters

Mehdi Mohammadi, Ali Azadeh, Morteza Saberi and Amir Azaron

International Journal of Business Forecasting and Marketing Intelligence, 2010, vol. 1, issue 3/4, 201-216

Abstract: Genetic algorithms (GAs) have been used in the clustering subject. Also, a clustering ensemble as one acceptable clustering method combines the results of multiple clustering methods on a given dataset and creates final clustering on the dataset. In this paper, genetic algorithm base on clustering ensemble (GACE) is introduced for finding optimal clusters. The most important property of our method is the ability to extract the number of clusters. With this ability, the need for data examination is removed, and then solving related problems will not be time consuming. GACE is applied to eight series of databases. Experimental results were compared with other four clustering methods. Data envelopment analysis (DEA) is used to compare methods. The results of DEA indicate that GACE is the best method. The four methods are co-association function and average link (CAL), co-association function and K-means (CK), hypergraph-partitioning algorithm (HGPA) and cluster-based similarity partitioning (CSPA).

Keywords: genetic algorithms; GAs; clustering ensembles; data envelopment analysis; DEA; co-association function; average link; K-means; hypergraph-partitioning algorithms; HGPA; cluster-based similarity partitioning; CSPA. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=36004 (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:ijbfmi:v:1:y:2010:i:3/4:p:201-216

Access Statistics for this article

More articles in International Journal of Business Forecasting and Marketing Intelligence from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().