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Clustering is a popular data mining technique that aims to partition an inputspace into multiple homogeneous regions. There exist several clusteringalgorithms in the literature. The performance of a clustering algorithm dependson its input parameters which can substantially affect the behavior of thealgorithm. Cluster validity indices determine the partitioning that best fitsthe underlying data. In bioinformatics, microarray gene expression technologyhas made it possible to measure the gene expression levels of thousands ofgenes simultaneously. Many genomic studies, which aim to analyze the functionsof some genes, highly rely on some clustering technique for grouping similarlyexpressed genes in one cluster or partitioning tissue samples based on similarexpression values of genes. In this work, an application package calledEXCLUVIS (gene EXpression data CLUstering and VISualization) has been developedusing MATLAB Graphical User Interface (GUI) environment for analyzing theperformances of different clustering algorithms on gene expression datasets. Inthis application package, the user needs to select a number of parameters suchas internal validity indices, external validity indices and number of clustersfrom the active windows for evaluating the performance of the clusteringalgorithms. EXCLUVIS compares the performances of K-means, fuzzy C-means,hierarchical clustering and multiobjective evolutionary clustering algorithms.Heatmap and cluster profile plots are used for visualizing the results.EXCLUVIS allows the users to easily find the goodness of clustering solutionsas well as provides visual representations of the clustering outcomes.

EXCLUVIS: A MATLAB GUI Software for Comparative Study of Clustering and Visualization of Gene Expression Data