  With the rapid advances of microarray technologies, large amounts of
high-dimensional gene expression data are being generated, which poses
significant computational challenges. A first step towards addressing this
challenge is the use of clustering techniques, which is essential in the data
mining process to reveal natural structures and identify interesting patterns
in the underlying data. A robust gene expression clustering approach to
minimize undesirable clustering is proposed. In this paper, Penalized Fuzzy
C-Means (PFCM) Clustering algorithm is described and compared with the most
representative off-line clustering techniques: K-Means Clustering, Rough
K-Means Clustering and Fuzzy C-Means clustering. These techniques are
implemented and tested for a Brain Tumor gene expression Dataset. Analysis of
the performance of the proposed approach is presented through qualitative
validation experiments. From experimental results, it can be observed that
Penalized Fuzzy C-Means algorithm shows a much higher usability than the other
projected clustering algorithms used in our comparison study. Significant and
promising clustering results are presented using Brain Tumor Gene expression
dataset. Thus patterns seen in genome-wide expression experiments can be
interpreted as indications of the status of cellular processes. In these
clustering results, we find that Penalized Fuzzy C-Means algorithm provides
useful information as an aid to diagnosis in oncology.
