心理学研究
Online ISSN : 1884-1082
Print ISSN : 0021-5236
ISSN-L : 0021-5236
原著
変数間の関係性を考慮してクラスター数を決定するk-means法の改良
豊田 秀樹池原 一哉
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ジャーナル フリー

2011 年 82 巻 1 号 p. 32-40

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抄録

In this article, we propose a non-hierarchical clustering method that can consider the relations between several variables and determine the optimal number of clusters. By utilizing the Mahalanobis distance instead of the Euclidean distance, which is calculated in k-means, we could consider the relations between several variables and obtain better groupings. Assuming that the data are samples from a mixture normal distribution, we could also calculate Akaike's information criterion (AIC) and the Bayesian information criterion (BIC) to determine the number of clusters. We used simulation and real data examples to confirm the usefulness of the proposed method. This method allows determination of the optimal number of clusters, considering the relations between several variables.

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© 2011 公益社団法人 日本心理学会
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