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Factor PD-Clustering

Tortora, Cristina; Gettler-Summa, Mireille; Palumbo, Francesco (2013), Factor PD-Clustering, in Lausen, Berthold; Van der Poel, Dirk; Ultsch, Alfred, Algorithms from and for Nature and Life, Springer : Berlin, p. 115-123. http://dx.doi.org/10.1007/978-3-319-00035-0_11

Type
Chapitre d'ouvrage
External document link
http://arxiv.org/abs/1106.3830v3
Date
2013
Book title
Algorithms from and for Nature and Life
Book author
Lausen, Berthold; Van der Poel, Dirk; Ultsch, Alfred
Publisher
Springer
Published in
Berlin
ISBN
978-3-319-00034-3
Number of pages
547
Pages
115-123
Publication identifier
http://dx.doi.org/10.1007/978-3-319-00035-0_11
Metadata
Show full item record
Author(s)
Tortora, Cristina
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Gettler-Summa, Mireille
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Palumbo, Francesco
DIPARTIMENTO DI TEORIE E METODI DELLE SCIENZE UMANE E SOCIALI
Abstract (EN)
Probabilistic Distance (PD) Clustering is a non parametric probabilistic method to find homogeneous groups in multivariate datasets with J variables and n units. PD Clustering runs on an iterative algorithm and looks for a set of K group centers, maximising the empirical probabilities of belonging to a cluster of the n statistical units. As J becomes large the solution tends to become unstable. This paper extends the PD-Clustering to the context of Factorial clustering methods and shows that Tucker3 decomposition is a consistent transformation to project original data in a subspace defined according to the same PD-Clustering criterion. The method consists of a two step iterative procedure: a linear transformation of the initial data and PD-clustering on the transformed data. The integration of the PD Clustering and the Tucker3 factorial step makes the clustering more stable and lets us consider datasets with large J and let us use it in case of clusters not having elliptical form.
Subjects / Keywords
Multivariate analysis; Exploratory data analysis; Clustering; Factorial clustering; Non hierarchical iterative clustering

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