Rough Set based Attribute Clustering for Sample Classification of Gene Expression Data

Attribute clustering is one of the unsupervised data mining applications which have been previously used to identify statistical dependence between subsets of variables where the attributes within the same cluster have high similarity, but within different clusters have high dissimilarity. In this paper, we focus our discussion on the rough set theory for attribute clustering. Rough set theory is a theory adopted to deal with rough and uncertain knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available, providing a new method for data classification. Although there are numerous methods of rough set and cluster analysis, as the data objects are changing continuously, we have to improve these relevant technologies over time, and propose creative theory in response, meeting the demands of application. Lastly, the experimental result of our proposed algorithm Rough Set based attribute Clustering for Sample Classification (RSCSC) is compared with some of the traditional attribute clustering methods and it is proved to be efficient in finding the meaningful, feasible and compact patterns. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education