Detecting the Number of Clusters in n-Way Probabilistic Clustering

Authors:
Zhaoshui He;Andrzej Cichocki;Shengli Xie;Kyuwan Choi
Affiliations:
RIKEN Brain Science Institute, Saitama and South China University of Technology, Guangzhou;RIKEN Brain Science Institute, Saitama, Polish Academy of Sciences, Warsaw and Warsaw University of Technology, Warsaw;South China University of Technology, Guangzhou;ATR Computational Neuroscience Laboratories, Kyoto
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2010

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Abstract

Recently, there has been a growing interest in multiway probabilistic clustering. Some efficient algorithms have been developed for this problem. However, not much attention has been paid on how to detect the number of clusters for the general n-way clustering (n\ge 2). To fill this gap, this problem is investigated based on n-way algebraic theory in this paper. A simple, yet efficient, detection method is proposed by eigenvalue decomposition (EVD), which is easy to implement. We justify this method. In addition, its effectiveness is demonstrated by the experiments on both simulated and real-world data sets.