Clustering multi-way data via adaptive subspace iteration

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.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

General Terms:
Algorithms, Experimentation, Performance

Keywords:
clustering, multi-way data, subspace, tensor