# Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data

arXiv: https://arxiv.org/abs/2212.14111 · HTML: https://mlsanity.com/paper/2212.14111/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 28 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [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)
- [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)
- [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)
- [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)

## Built on by (most cited first, 7 of 7)

- [Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning](https://mlsanity.com/paper/2301.00802/) (2023)
- [Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking](https://mlsanity.com/paper/2401.04266/) (2024)
- [Transfer Learning of Tabular Data by Finetuning Large Language Models](https://mlsanity.com/paper/2501.06863/) (2025)
- [ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science](https://mlsanity.com/paper/2412.18641/) (2024)
- [Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering](https://mlsanity.com/paper/2604.07085/) (2026)
- [Between-Sample Relationship in Learning Tabular Data Using Graph and Attention Networks](https://mlsanity.com/paper/2306.06772/) (2023)
- [Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data](https://mlsanity.com/paper/2608.00346/) (2026)

## Abstract (arXiv)

Deep learning methods in the literature are commonly benchmarked on image data sets, which may not be suitable or effective baselines for non-image tabular data. In this paper, we take a data-centric view to perform one of the first studies on deep embedding clustering of tabular data. Eight clustering and state-of-the-art embedding clustering methods proposed for image data sets are tested on seven tabular data sets. Our results reveal that a traditional clustering method ranks second out of eight methods and is superior to most deep embedding clustering baselines. Our observation aligns with the literature that conventional machine learning of tabular data is still a robust approach against deep learning. Therefore, state-of-the-art embedding clustering methods should consider data-centric customization of learning architectures to become competitive baselines for tabular data.