Factor PD-Clustering

  • Cristina Tortora
  • Mireille Gettler Summa
  • Francesco Palumbo
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


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.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Cristina Tortora
    • 1
    • 2
  • Mireille Gettler Summa
    • 2
  • Francesco Palumbo
    • 1
  1. 1.Università degli Studi di Napoli Federico IINaplesItaly
  2. 2.CEREMADE, CNRSUniversité Paris DauphineParisFrance

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