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Issue No. 11 - November (2010 vol. 32)
ISSN: 0162-8828
pp: 2006-2021
Zhaoshui He , RIKEN Brain Science Institute, Saitama and South China University of Technology, Guangzhou
Andrzej Cichocki , RIKEN Brain Science Institute, Saitama, Polish Academy of Sciences, Warsaw and Warsaw University of Technology, Warsaw
Shengli Xie , South China University of Technology, Guangzhou
Kyuwan Choi , ATR Computational Neuroscience Laboratories, Kyoto
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.
Multiway clustering, probabilistic clustering, hypergraph, parallel factor analysis (PARAFAC), model order selection, multiway array, higher order tensor, supersymmetric tensors, affinity arrays, enumeration of clusters, estimation of PARAFAC components, principal components enumeration.

A. Cichocki, S. Xie, Z. He and K. Choi, "Detecting the Number of Clusters in n-Way Probabilistic Clustering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 2006-2021, 2010.
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