Publication

Subspace K-means clustering

Timmerman, M. E., Ceulemans, E., De Roover, K. & Van Leeuwen, K., Dec-2013, In : Behavior Research Methods. 45, 4, p. 1011-1023 13 p.

Research output: Contribution to journalArticleAcademicpeer-review

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

Original languageEnglish
Pages (from-to)1011-1023
Number of pages13
JournalBehavior Research Methods
Volume45
Issue number4
Publication statusPublished - Dec-2013

    Keywords

  • Cluster analysis, Cluster recovery, Multivariate data, Reduced K-means, K-means, Factorial K-means, Mixtures of factor analyzers, MCLUST, PRINCIPAL COMPONENT ANALYSIS, HIGH-DIMENSIONAL DATA, PARENTAL BEHAVIOR, LOCAL OPTIMA, MODEL, ALGORITHM, COMPLEXITIES, PSYCHOLOGY, REDUCTION, FACTORIAL

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