Multi-Slice Clustering for 3-order Tensor Data

TL;DR

This paper introduces multi-slice clustering (MSC), a novel method for triclustering 3-order tensor data that avoids arbitrary cluster size specifications by analyzing spectral decompositions of tensor slices.

Contribution

The paper presents MSC, a new spectral-based triclustering method that automatically identifies clusters in 3D tensor data without predefined sizes.

Findings

Effective on synthetic data

Successfully applied to real-world data

Outperforms existing methods in accuracy

Abstract

Several methods of triclustering of three dimensional data require the specification of the cluster size in each dimension. This introduces a certain degree of arbitrariness. To address this issue, we propose a new method, namely the multi-slice clustering (MSC) for a 3-order tensor data set. We analyse, in each dimension or tensor mode, the spectral decomposition of each tensor slice, i.e. a matrix. Thus, we define a similarity measure between matrix slices up to a threshold (precision) parameter, and from that, identify a cluster. The intersection of all partial clusters provides the desired triclustering. The effectiveness of our algorithm is shown on both synthetic and real-world data sets.