  Clustering is a popular data mining technique that aims to partition an input
space into multiple homogeneous regions. There exist several clustering
algorithms in the literature. The performance of a clustering algorithm depends
on its input parameters which can substantially affect the behavior of the
algorithm. Cluster validity indices determine the partitioning that best fits
the underlying data. In bioinformatics, microarray gene expression technology
has made it possible to measure the gene expression levels of thousands of
genes simultaneously. Many genomic studies, which aim to analyze the functions
of some genes, highly rely on some clustering technique for grouping similarly
expressed genes in one cluster or partitioning tissue samples based on similar
expression values of genes. In this work, an application package called
EXCLUVIS (gene EXpression data CLUstering and VISualization) has been developed
using MATLAB Graphical User Interface (GUI) environment for analyzing the
performances of different clustering algorithms on gene expression datasets. In
this application package, the user needs to select a number of parameters such
as internal validity indices, external validity indices and number of clusters
from the active windows for evaluating the performance of the clustering
algorithms. 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 solutions
as well as provides visual representations of the clustering outcomes.
