# Interpretable Deep Clustering for Tabular Data

arXiv: https://arxiv.org/abs/2306.04785 · HTML: https://mlsanity.com/paper/2306.04785/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 72 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 25 of 25)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction](https://mlsanity.com/paper/2006.08558/) (2020)
- [Frequency Bias in Neural Networks for Input of Non-Uniform Density](https://mlsanity.com/paper/2003.04560/) (2020)
- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [SpectralNet: Spectral Clustering using Deep Neural Networks](https://mlsanity.com/paper/1801.01587/) (2018)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Neural Manifold Clustering and Embedding](https://mlsanity.com/paper/2201.10000/) (2022)
- [You Never Cluster Alone](https://mlsanity.com/paper/2106.01908/) (2021)
- [From Clustering to Cluster Explanations via Neural Networks](https://mlsanity.com/paper/1906.07633/) (2019)
- [Locally Sparse Neural Networks for Tabular Biomedical Data](https://mlsanity.com/paper/2106.06468/) (2021)
- [Pseudo-supervised Deep Subspace Clustering](https://mlsanity.com/paper/2104.03531/) (2021)
- [XAI Beyond Classification: Interpretable Neural Clustering](https://mlsanity.com/paper/1808.07292/) (2018)
- [Where to Pay Attention in Sparse Training for Feature Selection?](https://mlsanity.com/paper/2211.14627/) (2022)
- [Deep clustering with concrete k-means](https://mlsanity.com/paper/1910.08031/) (2019)
- [Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features](https://mlsanity.com/paper/2110.05306/) (2021)
- [Interpretable Clustering via Multi-Polytope Machines](https://mlsanity.com/paper/2112.05653/) (2021)
- [Composite Feature Selection using Deep Ensembles](https://mlsanity.com/paper/2211.00631/) (2022)
- [Support Recovery with Stochastic Gates: Theory and Application for Linear Models](https://mlsanity.com/paper/2110.15960/) (2021)
- [Feature selection revisited in the single-cell era](https://mlsanity.com/paper/2110.14329/) (2021)

## Built on by (most cited first, 19 of 19)

- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [Interpretable Clustering: A Survey](https://mlsanity.com/paper/2409.00743/) (2024)
- [ICR-RL: Deep Reinforcement Learning via In-Context Regression](https://mlsanity.com/paper/2509.11259/) (2025)
- [ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data](https://mlsanity.com/paper/2505.10704/) (2025)
- [Unveiling Multiple Descents in Unsupervised Autoencoders](https://mlsanity.com/paper/2406.11703/) (2024)
- [Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings](https://mlsanity.com/paper/2511.06961/) (2025)
- [NeurCAM: Interpretable Neural Clustering via Additive Models](https://mlsanity.com/paper/2408.13361/) (2024)
- [NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series](https://mlsanity.com/paper/2510.22397/) (2025)
- [Self Supervised Correlation-based Permutations for Multi-View Clustering](https://mlsanity.com/paper/2402.16383/) (2024)
- [Spectral Self-supervised Feature Selection](https://mlsanity.com/paper/2407.09061/) (2024)
- [Distributionally Robust Feature Selection](https://mlsanity.com/paper/2510.21113/) (2025)
- [Conditional Deep Canonical Time Warping](https://mlsanity.com/paper/2412.18234/) (2024)
- [i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data](https://mlsanity.com/paper/2603.24025/) (2026)
- [Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data](https://mlsanity.com/paper/2604.05857/) (2026)
- [Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering](https://mlsanity.com/paper/2604.10865/) (2026)
- [Cohort Organized Learning: Clustering Through Agreement](https://mlsanity.com/paper/2606.21743/) (2026)
- [Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data](https://mlsanity.com/paper/2608.00346/) (2026)
- [Population-Robust Feature Selection via Generalized Welfare Optimization](https://mlsanity.com/paper/2608.02887/) (2026)
- [GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering](https://mlsanity.com/paper/2608.07881/) (2026)

## 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.