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
External document linkhttp://arxiv.org/abs/1106.3830v3
Book titleAlgorithms from and for Nature and Life
Book authorLausen, Berthold; Van der Poel, Dirk; Ultsch, Alfred
Number of pages547
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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 / KeywordsMultivariate analysis; Exploratory data analysis; Clustering; Factorial clustering; Non hierarchical iterative clustering
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