Performance of an ensemble clustering algorithm on biological data sets

Abstract

Ensemble clustering is a promising approach that combines the results of multiple clustering algorithms to obtain a consensus partition by merging different partitions based upon well-defined rules. In this study, we use an ensemble clustering approach for merging the results of five different clustering algorithms that are sometimes used in bioinformatics applications. The ensemble clustering result is tested on microarray data sets and compared with the results of the individual algorithms. An external cluster validation index, adjusted rand index (C-rand), and two internal cluster validation indices; silhouette, and modularity are used for comparison purposes. © Association for Scientific Research.