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2014 | OriginalPaper | Chapter

Robustness and Stability Analysis of Factor PD-Clustering on Large Social Data Sets

Authors : Cristina Tortora, Marina Marino

Published in: Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Publisher: Springer International Publishing

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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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Metadata
Title
Robustness and Stability Analysis of Factor PD-Clustering on Large Social Data Sets
Authors
Cristina Tortora
Marina Marino
Copyright Year
2014
Book
Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Print ISBN: 978-3-319-06691-2

Electronic ISBN: 978-3-319-06692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-06692-9_29

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