A new approach for clustering gene expression time series data

Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper proposes a suitable dissimilarity measure for gene expression time series data sets. It also presents a graph-based clustering method for finding clusters in gene expression time series data using the new dissimilarity measure. A comparison with other similarity measures used for gene expression data is presented; the new dissimilarity measure is found effective. The clustering method is used in experiments that use real-life datasets and has been found to perform satisfactorily.