An improved method using k-means to determine the optimal number of clusters, considering the relations between several variables

Hideki Toyoda, Kazuya Ikehara

研究成果: Article査読

1 被引用数 (Scopus)

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.

本文言語English
ページ(範囲)32-40
ページ数9
ジャーナルShinrigaku Kenkyu
82
1
DOI
出版ステータスPublished - 2011 4

ASJC Scopus subject areas

  • 心理学(全般)

フィンガープリント

「An improved method using k-means to determine the optimal number of clusters, considering the relations between several variables」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル