Non-hierarchical Clustering of Decision Tables toward Rough Set-Based Group Decision Aid

In order to analyze the distribution of mind-sets (collections of evaluations) in a group, a hierarchical clustering of decision tables has been examined. By the method, we know clusters of mind-set but the clusters are not always optimal in some criterion. In this paper, we develop non-hierarchical clustering techniques for decision tables. In order to treat positive and negative evaluations to a common profile, we use a vector of rough membership values to represent individual opinion to a profile. Using rough membership values, we develop a K-means method as well as fuzzy c-means methods for clustering decision tables. We examined the proposed methods in clustering real world decision tables.