|
The k-means clustering technique: General considerations and implementation in Mathematica
Full text PDF
Bibliographic information:
BibTEX format
RIS format
XML format
APA style
Cited references information:
BibTEX format
APA style
Doi:
10.20982/tqmp.09.1.p015
Morissette, Laurence
, Chartier, Sylvain
15-24
Keywords:
statistics
, k-mean clustering
Tools: Mathematica
(data file)
 
(Appendix)
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.
|