Feb 19, 2020 · In this paper, we propose a co-clustering model for tensor data, where clustering of row (indexed from to n) and column (indexed from to d) entities is done ...

Feb 19, 2020 · Co-clustering aims to obtain homogeneous blocks leading to a straightforward simultaneous interpretation of row clusters and column clusters.

Missing: Tensor | Show results with:Tensor

Oct 22, 2024 · Co-clustering aims to obtain homogeneous blocks leading to a straightforward simultaneous interpretation of row clusters and column clusters.

Missing: Tensor | Show results with:Tensor

Sep 11, 2023 · Tensor Latent Block Model for Co-clustering [Boutalbi et al., 2019a]. In our first contri- bution, we rely on the latent block model (LBM) ...

All proposed algorithms are based on the latent block models and suitable to different types of data, sparse or not. They are successfully evaluated on ...

TensorClus (Tensor Clustering) is the first Python library aiming to cluster and co-clustering tensor data. It allows to easily perform tensor clustering ...

Ailem, M., Role, F., Nadif, M.: Model-based co-clustering for the effective handling of sparse data. Pattern Recognit. 72, 108–122 (2017)

Jan 5, 2021 · The MLBM performs a co-clustering such that the row-clusters partition is the same for all x(d) x ( d ) , and that there is a column-clusters ...