Abstract
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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Tortora, C., Summa, M.G., Palumbo, F. (2013). Factor PD-Clustering. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_11
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DOI: https://doi.org/10.1007/978-3-319-00035-0_11
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