Rough Clustering: An Alternative to Find Meaningful Clusters by Using the Reducts from a Dataset

Abstract

Rough Sets Theory has been applied to build classifiers by exploring symbolic relations in data. Indiscernibility relations combined with the concept notion, and the application of set operations, lead to knowledge discovery in an elegant and intuitive way. In this paper we argue that the indiscernibility relation has a strong appeal to be applied in clustering since itself is a sort of natural clustering in the n-dimensional space of attributes. We explore this fact to build a clustering scheme that discovers straight structures for clusters in the sub-dimensional space of the attributes. As the usual clustering process is a kind of search for concepts, the scheme here proposed provides a better description of such clusters allowing the analyst to figure out what cluster has meaning to be considered as a concept. The basic idea is to find reducts in a set of objects and apply them to any clustering procedure able to cope with discrete data. We apply the approach to a toy example of animal taxonomy in order to show its functionality.