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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Vydáno v:Tutorials in quantitative methods for psychology Ročník 9; číslo 1; s. 15 - 24
Hlavní autoři: Morissette, Laurence, Chartier, Sylvain
Médium: Journal Article
Jazyk:angličtina
Vydáno: Université d'Ottawa 01.02.2013
Témata:
k-mean clustering
Mathematica
statistics
ISSN:1913-4126, 1913-4126
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Shrnutí: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