Multi-Slice Clustering for 3-order Tensor Data

• URL: http://arxiv.org/abs/2109.10803v1
• Date: Wed, 22 Sep 2021 15:49:48 GMT
• Title: Multi-Slice Clustering for 3-order Tensor Data
• Authors: Dina Faneva Andriantsiory, Joseph Ben Geloun, Mustapha Lebbah
• Abstract summary: Several methods of triclustering of three dimensional data require the specification of the cluster size in each dimension. We propose a new method, namely the multi-slice clustering (MSC) for a 3-order tensor data set. The effectiveness of our algorithm is shown on both synthetic and real-world data sets.
• Score: 0.12891210250935145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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