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arXiv · 2109.10803

Multi-Slice Clustering for 3-order Tensor Data

Dina Faneva Andriantsiory·Joseph Ben Geloun·Mustapha Lebbah
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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.

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Dina Faneva Andriantsiory, Joseph Ben Geloun, Mustapha Lebbah. 2021-09-22. Multi-Slice Clustering for 3-order Tensor Data. https://arxiv.org/abs/2109.10803

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