We present a density based method for clustering gene expression data using a two-objective function. The method uses regulation information as well as a suitable dissimilarity measure to cluster genes into regions of higher density separated by sparser regions. The method has been tested on five benchmark microarray datasets and found to perform well in terms of homogeneity and z-score measures.
