  Clustering is one of the widely used data mining techniques for medical
diagnosis. Clustering can be considered as the most important unsupervised
learning technique. Most of the clustering methods group data based on distance
and few methods cluster data based on similarity. The clustering algorithms
classify gene expression data into clusters and the functionally related genes
are grouped together in an efficient manner. The groupings are constructed such
that the degree of relationship is strong among members of the same cluster and
weak among members of different clusters. In this work, we focus on a
similarity relationship among genes with similar expression patterns so that a
consequential and simple analytical decision can be made from the proposed
Fuzzy Soft Rough K-Means algorithm. The algorithm is developed based on Fuzzy
Soft sets and Rough sets. Comparative analysis of the proposed work is made
with bench mark algorithms like K-Means and Rough K-Means and efficiency of the
proposed algorithm is illustrated in this work by using various cluster
validity measures such as DB index and Xie-Beni index.
