mlsanityData to 7 Oct 2026

Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data

arXiv 2212.14111 ↗First posted 28 Dec 2022cs.LG cs.AI cs.NEcited by 7

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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.

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

Builds on

12 of 23 references.

References · 12

  1. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  2. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  3. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  4. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  5. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
  6. 2017Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimizationcited by 113
  7. 2018Deep k-Means: Jointly clustering with k-Means and learning representationscited by 50
  8. 2019Survey on Deep Neural Networks in Speech and Vision Systemscited by 37
  9. 2019Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Constructioncited by 13
  10. 2022Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Frameworkcited by 13
  11. 2020Joint Optimization of an Autoencoder for Clustering and Embeddingcited by 10
  12. 2022Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Datacited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 2.

7 papers.

  1. 2023Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learningcited by 12
  2. 2024Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarkingcited by 9
  3. 2025Transfer Learning of Tabular Data by Finetuning Large Language Modelscited by 4
  4. 2024ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Sciencecited by 3
  5. 2026Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clusteringcited by 2
  6. 2023Between-Sample Relationship in Learning Tabular Data Using Graph and Attention Networkscited by 1
  7. 2026Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Datacited by 0