The k-means clustering technique: General considerations and implementation in Mathematica

Data clustering techniques are valuable tools for researchers working with large databases of multivariate data. In this tutorial, we present a simple yet powerful one: the k-means clustering technique, through three different algorithms: the Forgy/Lloyd, algorithm, the MacQueen algorithm and the Ha...

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Veröffentlicht in:Tutorials in quantitative methods for psychology Jg. 9; H. 1; S. 15 - 24
Hauptverfasser: Morissette, Laurence, Chartier, Sylvain
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Université d'Ottawa 01.02.2013
Schlagworte:
k-mean clustering
Mathematica
statistics
ISSN:1913-4126, 1913-4126
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Data clustering techniques are valuable tools for researchers working with large databases of multivariate data. In this tutorial, we present a simple yet powerful one: the k-means clustering technique, through three different algorithms: the Forgy/Lloyd, algorithm, the MacQueen algorithm and the Hartigan and Wong algorithm. We then present an implementation in Mathematica and various examples of the different options available to illustrate the application of the technique.
ISSN:1913-4126
1913-4126
DOI:10.20982/tqmp.09.1.p015