Abstract
Recently, a large number of techniques of numerical taxonomy or cluster analysis are proposed and studied by many workers, and the needs for these techniques arise in many fields of applied science. Especially, the method of hierarchical cluster analysis is used widely, since all of these methods are suitable for various types of data and can be simply carried out. However, though the investigations for evaluation or comparison of these method are only little discussed in formal works, it is quite important and necessary to discuss them in order to study cluster analysis. Furthermore, we propose the fuzzy distance which is a new index of evaluating and comparing relationship between two relation matrices, the original similarity matrix S and S* which is derived from S by executing hierarchical clustering algorithms. Finally, numerical examples for several artificial datas are investigated by four well-known clustering methods.
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