Multi-Slice Clustering for 3-order Tensor Data
This addresses a specific issue in tensor data analysis for researchers in data mining or machine learning, but it is incremental as it builds on existing triclustering methods.
The authors tackled the problem of arbitrary cluster size specification in triclustering of 3-order tensor data by proposing a multi-slice clustering method, which identifies clusters through similarity measures between matrix slices and shows effectiveness on synthetic and real-world datasets.
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.