Genetic algorithm-based clustering ensemble: determination number of clusters

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).