Abstract
A classification algorithm of decision tables has been proposed to investigate major opinions and the distribution of opinions in the given group of decision tables. In the approach, an agglomerative hierarchical clustering algorithm is applied using a similarity between clusters of decision tables. Therefore, the algorithm terminates after all decision tables compose a cluster so that the proper number of clusters cannot be determined automatically. In this paper, a few clustering methods based on the rational choice theory are proposed. It is shown that the algorithm can terminate before all decision tables compose a cluster so that the number of clusters is automatically determined.
