Behaviormetrika
Online ISSN : 1349-6964
Print ISSN : 0385-7417
ISSN-L : 0385-7417
A HIERARCHICAL CLUSTERING METHOD FOR RANK ORDER DATA
Takayuki Saito
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ジャーナル 認証あり

1980 年 7 巻 8 号 p. 23-39

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For a data matrix composed of subjects by rank orders, a hierarchical clustering method is presented, which partitions subjects into statistically homogeneous clusters on the basis of Kendall's coefficient of concordance W. The algorithm has been found to work successfully. Illustrative examples of the clustering of real data are offered. The method is useful for both preliminary analysis and confirmatory analysis of nonmetric multidimensional scaling of rank order data, especially in large samples. This method is also compared with other hierarchical clustering methods.

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© The Behaviormetric Society of Japan
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