Clustering multi-way data via adaptive subspace iteration

Authors:
Wei Peng;Tao Li;Bo Shao
Affiliations:
Xerox, Rochester, NY, USA;FIU, Miami, FL, USA;FIU, Miami, FL, USA
Venue:
Proceedings of the 17th ACM conference on Information and knowledge management
Year:
2008

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Abstract

Clustering multi-way data is a very important research topic due to the intrinsic rich structures in real-world datasets. In this paper, we propose the subspace clustering algorithm on multi-way data, called ASI-T (Adaptive Subspace Iteration on Tensor). ASI-T is a special version of High Order SVD (HOSVD), and it simultaneously performs subspace identification using 2DSVD and data clustering using K-Means. The experimental results on synthetic data and real-world data demonstrate the effectiveness of ASI-T.