MicroRNAs (miRNAs) are small non-coding molecules that have been shown to play key roles in regulating cellular development and to be involved in various diseases. By interfering with their target mRNAs, these molecules inhibit the expression of proteins, either by destabilizing the mRNA molecule or by preventing its translation. It has been suggested that each miRNA can target hundreds of mRNAs, and that one mRNA can be targeted by several miRNAs. This makes it extremely complex to determine the roles of specific miRNAs in the regulation of translation of mRNA. Recent advancements in microarray technology have made large-scale monitoring of miRNA expression possible. However, the size and complexity of these data sets make them challenging to analyze, and improved algorithms are therefore required to facilitate the analysis. In this paper, we present a novel clustering algorithm that uses an Information Fusion (IF) approach to cluster miRNA data, allowing for multiple types of input data to guide the clustering. For evaluation of the algorithm, we used miRNA expression data from human embryonic stem cells and cardiomyocyte-like cells derived thereof. Clusters obtained when using the multiple input data approach were compared to those generated when using only the expression data. Our results show that it is beneficial to include various types of genomic data as input to the clustering process, since it results in clusters of increased biological relevance.