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Three‐Way Data Analysis With Explainable Tucker3 Clustering (XT3Clus)

Mariaelena Bottazzi Schenone et al.

Applied Stochastic Models in Business and Industry2026https://doi.org/10.1002/asmb.70069article
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0.50

What the paper says

In an era of increasingly complex data, three‐way arrays capturing information across units, variables and occasions are ubiquitous in fields from chemometrics to finance. However, extracting meaningful and interpretable patterns from such data remain a significant challenge. To address this, we introduce the Explainable Tucker3 Clustering (XT3Clus) methodology. XT3Clus performs clustering on units while simultaneously identifying explainable components for variables and/or occasions, significantly enhancing model interpretability. This approach functions as a constrained Tucker3 model, where each dimension is forced to contribute to a single component. The framework supports fully confirmatory, exploratory or hybrid analytical strategies. The optimization of the objective function is carried out by an efficient Alternating Least Squares algorithm. Finally, we propose a novel quantitative metric to evaluate the interpretability of a solution and confirm the practical utility of XT3Clus in three real‐world scenarios.

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https://doi.org/https://doi.org/10.1002/asmb.70069

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@article{mariaelena2026,
  title        = {{Three‐Way Data Analysis With Explainable Tucker3 Clustering (XT3Clus)}},
  author       = {Mariaelena Bottazzi Schenone et al.},
  journal      = {Applied Stochastic Models in Business and Industry},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/asmb.70069},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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