Clustering Individual Opinions Based on Rough Sets

Abstract

Cluster analysis of individual opinions given as decision tables is proposed in this paper. Each decision table is regarded as opinions of an individual given in terms of examples. In order to grasp major opinions in the whole group of individuals, we propose the application of an agglomerative hierarchical clustering to a set of decision tables. Major opinions are obtained as common decision rules included in the obtained clusters. Some similarities are defined to evaluate to what extent the members of two clusters agreed. The clustering results by different similarities are compared from the viewpoints of stability and group cohesiveness in clusters.