# Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning

arXiv: https://arxiv.org/abs/2301.00802 · HTML: https://mlsanity.com/paper/2301.00802/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 26 of 26)

- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [On Embeddings for Numerical Features in Tabular Deep Learning](https://mlsanity.com/paper/2203.05556/) (2022)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Well-tuned Simple Nets Excel on Tabular Datasets](https://mlsanity.com/paper/2106.11189/) (2021)
- [Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization](https://mlsanity.com/paper/1704.06327/) (2017)
- [Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning](https://mlsanity.com/paper/2106.02584/) (2021)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Deep k-Means: Jointly clustering with k-Means and learning representations](https://mlsanity.com/paper/1806.10069/) (2018)
- [Survey on Deep Neural Networks in Speech and Vision Systems](https://mlsanity.com/paper/1908.07656/) (2019)
- [Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction](https://mlsanity.com/paper/1901.07752/) (2019)
- [Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework](https://mlsanity.com/paper/2202.13734/) (2022)
- [Joint Optimization of an Autoencoder for Clustering and Embedding](https://mlsanity.com/paper/2012.03740/) (2020)
- [Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data](https://mlsanity.com/paper/2205.08358/) (2022)
- [Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data](https://mlsanity.com/paper/2212.14111/) (2022)
- [C3: Cross-instance guided Contrastive Clustering](https://mlsanity.com/paper/2211.07136/) (2022)
- [Subspace-Contrastive Multi-View Clustering](https://mlsanity.com/paper/2210.06795/) (2022)

## Built on by (most cited first, 12 of 12)

- [ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data](https://mlsanity.com/paper/2505.10704/) (2025)
- [A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning](https://mlsanity.com/paper/2305.01507/) (2023)
- [Transfer Learning of Tabular Data by Finetuning Large Language Models](https://mlsanity.com/paper/2501.06863/) (2025)
- [LATTLE: LLM Attention Transplant for Transfer Learning of Tabular Data Across Disparate Domains](https://mlsanity.com/paper/2511.06161/) (2025)
- [Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering](https://mlsanity.com/paper/2604.07085/) (2026)
- [Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classification](https://mlsanity.com/paper/2412.00238/) (2024)
- [Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer](https://mlsanity.com/paper/2504.14610/) (2025)
- [An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter](https://mlsanity.com/paper/2511.17983/) (2025)
- [Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering](https://mlsanity.com/paper/2604.10865/) (2026)
- [PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering](https://mlsanity.com/paper/2605.08673/) (2026)
- [Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data](https://mlsanity.com/paper/2608.00346/) (2026)
- [Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data](https://mlsanity.com/paper/2608.28209/) (2026)

## Abstract (arXiv)

Deep learning methods are primarily proposed for supervised learning of images or text with limited applications to clustering problems. In contrast, tabular data with heterogeneous features pose unique challenges in representation learning, where deep learning has yet to replace traditional machine learning. This paper addresses these challenges in developing one of the first deep clustering methods for tabular data: Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS). G-CEALS is an unsupervised deep clustering framework for learning the parameters of multivariate Gaussian cluster distributions by iteratively updating individual cluster weights. The G-CEALS method presents average rank orderings of 2.9(1.7) and 2.8(1.7) based on clustering accuracy and adjusted Rand index (ARI) scores on sixteen tabular data sets, respectively, and outperforms nine state-of-the-art clustering methods. G-CEALS substantially improves clustering performance compared to traditional K-means and GMM, which are still de facto methods for clustering tabular data. Similar computationally efficient and high-performing deep clustering frameworks are imperative to reap the myriad benefits of deep learning on tabular data over traditional machine learning.