MicroRNAs (miRNAs) are short, endogenous RNAs having ability to regulate gene expression at the post-transcriptional level. Various studies have revealed that miRNAs tend to cluster on chromosomes. Members of a cluster that are at close proximity on chromosome are highly likely to be processed as cotranscribed units. Therefore, a large proportion of miRNAs are co-expressed. Expression profiling of miRNAs generates a huge volume of data. Complicated networks of miRNA-mRNA interaction create a big challenge for scientists to decipher this huge expression data. In order to extract meaningful information from expression data, this paper presents the application of robust rough-fuzzy c-means (rRFCM) algorithm to discover co-expressed miRNA clusters. The rRFCM algorithm comprises a judicious integration of rough sets, fuzzy sets, and c-means algorithm. The effectiveness of the rRFCM algorithm and different initialization methods, along with a comparison with other related methods, is demonstrated on three miRNA microarray expression data sets using Silhouette index, Davies-Bouldin index, Dunn index, β index, and gene ontology based analysis. © 2012 IEEE.