The Japanese Journal of Psychology
Online ISSN : 1884-1082
Print ISSN : 0021-5236
ISSN-L : 0021-5236
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An improved method using k-means to determine the optimal number of clusters, considering the relations between several variables
Hideki ToyodaKazuya Ikehara
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2011 Volume 82 Issue 1 Pages 32-40

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Abstract
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 The Japanese Psychological Association
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