DNA Microarray Data Clustering Based on Temporal Variation: FCV with TSD Preclustering

The aim of this paper is to present a new clustering algorithm for short time-series gene expression data that is able to characterize temporal relations in the clustering environment (i.e., data-space), which is not achieved by other conventional clustering algorithms such as k-means or hierarchical clustering. The algorithm called fuzzy cvarieties clustering with Transitional State Discrimination preclustering (FCV-TSD) is a two step-approach which identifies groups of points ordered in a line configuration in particular locations and orientations of the data-space that correspond to similar expressions in the time domain. We present the validation of the algorithm with both artificial and real experimental data sets, where k-means and random clustering are used for comparison. The performance is evaluated with a measure for internal cluster correlation and the geometrical properties of the clusters; showing that the TSD-FCV algorithm has better performance than the k-means algorithm on both data sets.