Abstract
Factor clustering methods were proposed to cluster large data sets. Among them factor probabilistic distance clustering (FPDC) shows interesting performance. The method is based on two main steps: a Tucker3 decomposition of the distance array and probabilistic distance (PD) clustering on the resulting factors. The aim of this paper is to apply FPDC on behavioral and social data sets of large dimensions, to obtain homogeneous and well-separated clusters of individuals. The scope is to evaluate the stability and the robustness of the method dealing with these data sets. Stability of results is referred to the invariance of results in each iteration of the method. Robustness is referred to the sensitivity of the method to errors in data. These characteristics of the method are evaluated using bootstrap resampling.
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