Tensor Co-clustering: A Parameter-less Approach

Tensors co-clustering has been proven useful in many applications, due to its ability of coping with high-dimensional data and sparsity. However, setting up a co-clustering algorithm properly requires the specification of the desired number of clusters for each mode as input parameters. To face this issue, we propose a tensor co-clustering algorithm that does not require the number of desired co-clusters as input, as it optimizes an objective function based on a measure of association across discrete random variables that is not affected by their cardinality. The effectiveness of our algorithm is shown on real-world datasets, also in comparison with state-of-the-art co-clustering methods