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

Hideki Toyoda, Kazuya Ikehara

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)32-40
Number of pages9
JournalShinrigaku Kenkyu
Volume82
Issue number1
DOIs
Publication statusPublished - 2011 Apr

Keywords

  • Clustering
  • Mahalanobis distance
  • Mixture distribution
  • Number of clusters

ASJC Scopus subject areas

  • Psychology(all)

Fingerprint Dive into the research topics of 'An improved method using k-means to determine the optimal number of clusters, considering the relations between several variables'. Together they form a unique fingerprint.

  • Cite this