 Degrees of freedom and model selection for k-means clusteringDavid P. Hofmeyr Computational Statistics & Data Analysis, 2020, vol. 149, issue C Abstract: A thorough investigation into the model degrees of freedom in k-means clustering is conducted. An extension of Stein’s lemma is used to obtain an expression for the effective degrees of freedom in the k-means model. Approximating the degrees of freedom in practice requires simplifications of this expression, however empirical studies evince the appropriateness of the proposed approach. The practical relevance of this new degrees of freedom formulation for k-means is demonstrated through model selection using the Bayesian Information Criterion. The reliability of this method is then validated through experiments on simulated data as well as on a large collection of publicly available benchmark data sets from diverse application areas. Comparisons with popular existing techniques indicate that this approach is extremely competitive for selecting high quality clustering solutions. Keywords: Clustering; k-means; Model selection; Cluster number determination; Degrees of freedom; Bayesian Information Criterion; Penalised likelihood (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:149:y:2020:i:c:s0167947320300657 DOI: 10.1016/j.csda.2020.106974 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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