mlsanityData to 7 Oct 2026

Interpretable Deep Clustering for Tabular Data

arXiv 2306.04785 ↗First posted 7 Jun 2023cs.LG stat.MLcited by 19

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Abstract (arXiv)

Clustering is a fundamental learning task widely used as a first step in data analysis. For example, biologists use cluster assignments to analyze genome sequences, medical records, or images. Since downstream analysis is typically performed at the cluster level, practitioners seek reliable and interpretable clustering models. We propose a new deep-learning framework for general domain tabular data that predicts interpretable cluster assignments at the instance and cluster levels. First, we present a self-supervised procedure to identify the subset of the most informative features from each data point. Then, we design a model that predicts cluster assignments and a gate matrix that provides cluster-level feature selection. Overall, our model provides cluster assignments with an indication of the driving feature for each sample and each cluster. We show that the proposed method can reliably predict cluster assignments in biological, text, image, and physics tabular datasets. Furthermore, using previously proposed metrics, we verify that our model leads to interpretable results at a sample and cluster level. Our code is available at https://github.com/jsvir/idc.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 25 of its 72 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

25 of 72 references.

References · 25

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  3. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  4. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  5. 2016Variational Deep Embedding: An Unsupervised and Generative Approach to Clusteringcited by 219
  6. 2017OpenML Benchmarking Suitescited by 169
  7. 2020Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reductioncited by 141
  8. 2020Frequency Bias in Neural Networks for Input of Non-Uniform Densitycited by 140
  9. 2021SPICE: Semantic Pseudo-labeling for Image Clusteringcited by 104
  10. 2018SpectralNet: Spectral Clustering using Deep Neural Networkscited by 94
  11. 2019Concrete Autoencoders for Differentiable Feature Selection and Reconstructioncited by 90
  12. 2023Synthcity: facilitating innovative use cases of synthetic data in different data modalitiescited by 76
13 more
  1. 2022Neural Manifold Clustering and Embeddingcited by 44
  2. 2021You Never Cluster Alonecited by 38
  3. 2019From Clustering to Cluster Explanations via Neural Networkscited by 34
  4. 2021Locally Sparse Neural Networks for Tabular Biomedical Datacited by 34
  5. 2021Pseudo-supervised Deep Subspace Clusteringcited by 29
  6. 2018XAI Beyond Classification: Interpretable Neural Clusteringcited by 24
  7. 2022Where to Pay Attention in Sparse Training for Feature Selection?cited by 15
  8. 2019Deep clustering with concrete k-meanscited by 11
  9. 2021Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Featurescited by 11
  10. 2021Interpretable Clustering via Multi-Polytope Machinescited by 11
  11. 2022Composite Feature Selection using Deep Ensemblescited by 11
  12. 2021Support Recovery with Stochastic Gates: Theory and Application for Linear Modelscited by 8
  13. 2021Feature selection revisited in the single-cell eracited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 6; 2026: 7.

19 papers.

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  1. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  2. 2024Interpretable Clustering: A Surveycited by 8
  3. 2025ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Datacited by 6
  4. 2025ICR-RL: Deep Reinforcement Learning via In-Context Regressioncited by 5
  5. 2025Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settingscited by 5
  6. 2024Unveiling Multiple Descents in Unsupervised Autoencoderscited by 3
  7. 2024NeurCAM: Interpretable Neural Clustering via Additive Modelscited by 3
  8. 2025NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Seriescited by 2
  9. 2024Self Supervised Correlation-based Permutations for Multi-View Clusteringcited by 1
  10. 2024Spectral Self-supervised Feature Selectioncited by 1
  11. 2025Distributionally Robust Feature Selectioncited by 1
  12. 2024Conditional Deep Canonical Time Warpingcited by 0
7 more
  1. 2026i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Datacited by 0
  2. 2026Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Datacited by 0
  3. 2026Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clusteringcited by 0
  4. 2026Cohort Organized Learning: Clustering Through Agreementcited by 0
  5. 2026Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Datacited by 0
  6. 2026Population-Robust Feature Selection via Generalized Welfare Optimizationcited by 0
  7. 2026GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clusteringcited by 0