Fuzzy Clustering of Decision Tables

Abstract

In order to analyze the distribution of individual mind-sets in a group, a hierarchical clustering of decision tables has been proposed. In the method, we can know how the individuals form clusters but the clusters are not always optimal in some criterion. In this paper, we develop non-hierarchical clustering techiniques for clustering of decision tables. We use a vector of rough membership values to represent individual opinon to a profile. Using rough membetship values, we develop fuzzy c-means methods for clustering decision tables. We examined the proposed methods in the clustering real world decision tables.