著者
豊田 秀樹 池原 一哉
出版者
公益社団法人 日本心理学会
雑誌
心理学研究 (ISSN:00215236)
巻号頁・発行日
vol.82, no.1, pp.32-40, 2011 (Released:2011-08-29)
参考文献数
26
被引用文献数
1 1

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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[kmeans][statistics] マハラノビス距離

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https://t.co/cPhQR51OfO 頑張って読む
最適なクラスタ数でk-means法をやるのにx-means法があるってわかった。他にも改良k-means法なんてあって、これ結構良いっぽい? https://t.co/K2Zc8LSRGJ

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