A COMPARISON OF K-MEANS AND FUZZY C-MEANS USING BACKGROUND KNOWLEDGE

J. Goddard, S.G. de los Cobos Silva and M.A. Gutiérrez Andrade
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M.A. Gutiérrez Andrade: Universidad Autónoma Metropolitana

Fuzzy Economic Review, 2006, vol. XI, issue 2, 3-16

Abstract: Relevant Component Analysis has been introduced recently as a way to incorporate a priori information, such as class or preference information, that may exist for a given data set. The method uses this information to define a new Mahalanobis distance metric on the data space. The purpose of the present paper is to investigate the difference, if any, that Relevant Component Analysis makes when applied in conjunction with the clustering algorithms of k-means or fuzzy c-means. Results are given for six standard data sets.

Keywords: Consumer behaviour; marketing modelling; model estimation; structural equation modelling; fuzzy association rules; knowledge discovery (search for similar items in EconPapers)
JEL-codes: G17 (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:fzy:fuzeco:v:xi:y:2006:i:2:p:3-16

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