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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Bibliographic Details
Published in:	Tutorials in quantitative methods for psychology Vol. 9; no. 1; pp. 15 - 24
Main Authors:	Morissette, Laurence, Chartier, Sylvain
Format:	Journal Article
Language:	English
Published:	Université d'Ottawa 01.02.2013
Subjects:	k-mean clustering Mathematica statistics
ISSN:	1913-4126, 1913-4126
Online Access:	Get full text
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Summary:	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