Knowledge-oriented clustering for decision support

Abstract

Cluster analysis is traditionally an unsupervised data. reduction technique. However, by unifying ideas from both cluster analysis and rough set theory, the inherent structure of a data set can be expressed as a rule set which, in turn, can be modified in a supervised manner to obtain a decision rule set with minimal ambiguity. This process of encasing knowledge extraction in an algorithmic framework results in an optimal process for decision support.