The aim of the present paper is to reconsider and re-interpret the iterative factor clustering for binary data in a two-fold way: a) define the dimension reduction step in the procedure as a statistical model based on a probability distribution; b) relax the hard classification (nonoverlapping) produced by the K-means, and allow for partial classification of respondents, that can be assigned to multiple clusters, with a different degree of membership.

Iterative factor clustering of categorical data reconsidered

Iodice D’Enza Alfonso
;
Palumbo Francesco
2015

Abstract

The aim of the present paper is to reconsider and re-interpret the iterative factor clustering for binary data in a two-fold way: a) define the dimension reduction step in the procedure as a statistical model based on a probability distribution; b) relax the hard classification (nonoverlapping) produced by the K-means, and allow for partial classification of respondents, that can be assigned to multiple clusters, with a different degree of membership.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/744245
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