TensorClus: A python library for tensor (Co)-clustering

Boutalbi, Rafika; Labiod, Lazhar; Nadif, Mohamed

doi:10.1016/j.neucom.2021.09.036

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…[Boutalbi et al, 2020] extended LBM to the case of a tri-dimensional tensor data by proposing the so-called Tensor LBM (TLBM) approach, while considering continuous, binary, and contingency tables data sets. They developed a variational EM algorithm and also developed a specific open-source Python package (TensorClus) [Boutalbi et al, 2022].…”

Section: Multiway Clusteringmentioning

confidence: 99%

A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges

2023

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Model-based co-clustering can be seen as a particularly valuable extension of model-based clustering for three main reasons: (1) while allowing parsimoniously a drastic reduction of both the number of lines/individuals and columns/variables of a data set, (2) it also allows interpretability of such a resulting reduced data set since initial individuals and features meaning is preserved in this latter; (3) moreover it benefits from the powerful mathematical statistics theory for both estimation and model selection. Hence, many authors produced new advances on this topic in the recent years, and this paper offers a general update of the related literature. In addition, it is the opportunity to pass two messages, supported by specific research materials: (1) co-clustering still requires some new and motivating researches for fixing some well-identified estimation issues, (2) co-clustering is probably one of the most promising clustering approach to be addressed in the (very) high dimension setting, which corresponds to the global trend on modern data sets.

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Section: Multiway Clusteringmentioning

confidence: 99%

A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges

2023

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“…, 𝑣 described in the previous section as part of a tensor X of size 𝑛 × 𝑛 × 𝑣. This tensor is used as input to tensor-based clustering techniques which are PARAFAC [17], TUCKER [30], TSPLBM 3 [6][7][8] and our proposed algorithm TGM.…”

Section: Experimental Settingsmentioning

confidence: 99%

Tensor-based Graph Modularity for Text Data Clustering

Boutalbi

Ait-Saada

Iurshina

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Graphs are used in several applications to represent similarities between instances. For text data, we can represent texts by different features such as bag-of-words, static embeddings (Word2vec, GloVe, etc.), and contextual embeddings (BERT, RoBERTa, etc.), leading to multiple similarities (or graphs) based on each representation. The proposal posits that incorporating the local invariance within every graph and the consistency across different graphs leads to a consensus clustering that improves the document clustering. This problem is complex and challenged with the sparsity and the noisy data included in each graph. To this end, we rely on the modularity metric, which effectively evaluates graph clustering in such circumstances. Therefore, we present a novel approach for text clustering based on both a sparse tensor representation and graph modularity. This leads to cluster texts (nodes) while capturing information arising from the different graphs. We iteratively maximize a Tensor-based Graph Modularity criterion. Extensive experiments on benchmark text clustering datasets are performed, showing that the proposed algorithm referred to as Tensor Graph Modularity -TGM-outperforms other baseline methods in terms of clustering task. The source code is available at https://github.com/TGMclustering/TGMclustering.

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Fast parameterless prototype-based co-clustering

Battaglia,

Peiretti,

Pensa

2023

Mach Learn

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Tensor co-clustering algorithms have been proven useful in many application scenarios, such as recommender systems, biological data analysis and the analysis of complex and evolving networks. However, they are significantly affected by wrong parameter configurations, since, at the very least, they require the cluster number to be set for each mode of the matrix/tensor, although they typically have other algorithm-specific hyper-parameters that need to be fine-tuned. Among the few known objective functions that can be optimized without setting these parameters, the Goodman–Kruskal $$\tau $$ τ —a statistical association measure that estimates the strength of the link between two or more discrete random variables—has proven its effectiveness in complex matrix and tensor co-clustering applications. However, its optimization in a co-clustering setting is tricky and, so far, has leaded to very slow and, at least in some specific but not unfrequent cases, inaccurate algorithms, due to its normalization term. In this paper, we investigate some interesting mathematical properties of $$\tau $$ τ , and propose a new simplified objective function with the ability of discovering an arbitrary and a priori unspecified number of good-quality co-clusters. Additionally, the new objective function definition allows for a novel prototype-based optimization strategy that enables the fast execution of matrix and higher-order tensor co-clustering. We show experimentally that the new algorithm preserves or even improves the quality of the discovered co-clusters by outperforming state-of-the-art competing approaches, while reducing the execution time by at least two orders of magnitude.

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TensorClus: A python library for tensor (Co)-clustering

Cited by 6 publications

References 19 publications

A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges

A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges

Tensor-based Graph Modularity for Text Data Clustering

Fast parameterless prototype-based co-clustering

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