Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

Manar D. Samad; Md. Kamrozzaman Bhuiyan; Pulock Das; Yina Hou

REVIEW 4 major objections 5 minor 29 references

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Unsupervised cluster ensembles recover true classes from imbalanced tabular data more reliably than single methods.

desk verdict Useful first benchmark of deep clustering on imbalanced tabular data with one clever ensemble, but the paper overclaims a supervised comparison and pokes a hole in its own KGT selection. read the letter →

arxiv 2608.00346 v1 pith:SJS73M6Z submitted 2026-07-31 cs.LG

Pulock Das , Yina Hou , Md. Kamrozzaman Bhuiyan , Manar D. Samad This is my paper · ORCID

classification cs.LG

keywords deepclusteringclusterensembleclassimbalancetabulardataG-CEALS-EEKGTunsupervisedlearningembeddingdimensions

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Class imbalance biases supervised classifiers toward the majority class, so this paper asks whether clustering without labels can avoid the problem. It compares six existing clustering methods and two new ensembles on 16 binary tabular datasets under natural and artificially induced imbalance, from 1:1 to 1:15. The central claim is that the ensembles—G-CEALS-EE, which aligns and averages cluster assignments across autoencoder embedding dimensions, and KGT, which majority-votes K-means, G-CEALS-EE, and TabClustPFN—achieve the best average performance in accuracy, normalized mutual information, and adjusted Rand index. If this is right, deep clustering offers a label-free route to recovering minority classes in imbalanced tabular data, without the sacrifices of undersampling or the distortions of oversampling.

What carries the argument

The alignment rule of Equation 1 is the load-bearing piece: cluster labels are arbitrary numbers, so before any aggregation the binary assignments from each embedding (for G-CEALS-EE) or each method (for KGT) are compared with a reference—the r=2 embedding or K-means—and flipped unless they agree on at least half the samples. This turns incomparable labelings into a common vote. Averaging the aligned binary assignments across dimensions and thresholding at 0.5 yields the ensemble label; KGT replaces averaging with majority voting across three aligned methods.

What would settle it

Split the 16 datasets into two halves: rank the individual methods on one half, build KGT from the top three found there, evaluate both KGT and the single methods on the other half, and repeat across many splits. If KGT no longer beats the best individual method on average, the reported ensemble resilience is a test-set selection artifact.

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Extended reading notes

Core claim

The paper claims that combining cluster assignments from several unsupervised views outperforms any single clustering method under class imbalance. G-CEALS-EE varies the embedding dimension of the Gaussian-cluster autoencoder G-CEALS, treats the two-dimensional embedding as the reference, flips each other embedding's binary assignments whenever they match the reference on fewer than half the samples, then averages the aligned assignments and thresholds at 0.5. KGT takes K-means, G-CEALS-EE, and TabClustPFN, aligns their assignments against K-means in the same way, and labels each sample by majority vote. On the 16 datasets, both ensembles tie for the best overall rank and prove the most stab

Load-bearing premise

The KGT ensemble's edge depends on picking its three members using the same 16 datasets that later measure its performance; if the member choice were made on separate data, the ensemble advantage could shrink or disappear.

Editorial extensions

If this is right

On the 16 binary tabular datasets, G-CEALS-EE and KGT tie for the best overall average rank across ACC, NMI, and ARI, ahead of every individual clustering method.
G-CEALS-EE is the most stable method under very high imbalance, taking best NMI and ARI and second-best ACC in that group, while TabClustPFN is the most imbalance-resilient individual method for NMI.
The ensembles deliver these results without labels, so they avoid class-weight tuning and do not require discarding majority samples or synthesizing minority samples.
Individual methods have complementary weaknesses—TabClustPFN's ACC is poor, QGRL degrades under imbalance, DCSS optimizes silhouette but not label agreement—so ensembling compensates rather than relying on any one objective.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 50% flip rule depends on having exactly two clusters; extending to multiclass would need a permutation-alignment strategy such as Hungarian matching across runs, a natural next step.
Because KGT's members are chosen by ranking on the same 16 datasets used for evaluation, the reported advantage may partly reflect selection on the test set; ranking members on a validation split would test how much of the ensemble benefit generalizes.
The alignment-plus-average trick is not tied to G-CEALS specifically: any autoencoder-style method that produces clusterings at several latent dimensions could adopt the same recipe, so the approach could raise the imbalanced-data ceiling of other deep clusterers.
If the result holds, clustering ensembles could serve as a label-efficient first pass in domains with scarce labels—medical records, fraud detection—where minority classes are exactly the ones supervised models miss.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my take.

The paper is worth reading for the comparison, not for the headline. It puts six existing deep clustering methods on 16 binary tabular datasets under controlled imbalance and adds two ensemble strategies. The systematic benchmark with induced 1:1 to 1:15 ratios is genuinely new and useful, and the G-CEALS-EE idea—aligning cluster labels across embedding dimensions and averaging—is simple, sensible, and looks like a real improvement over G-CEALS, which is known to be sensitive to embedding dimension. KGT is a majority vote of three methods, but it has a selection problem.

The soft spots are real. First, the abstract claims deep clustering 'can serve as a strong alternative to supervised classification,' yet no supervised classifier is run anywhere. That claim is untested. Fixing other issues would not fix this; the authors need logistic regression, XGBoost, or a standard classifier on the same splits. Second, KGT is built by ranking methods on the same 16 datasets used for evaluation and taking the top three. That is selection on the test set. The paper states labels are not used for hyperparameter optimization, but member selection uses the evaluation metrics on those same datasets, so that statement is misleading. G-CEALS-EE does not have this flaw and stands on its own. Third, there are no significance tests. Table 2 shows min-max normalized scores averaged within groups; error bars in Figure 1 are large, and without paired tests or absolute metric values, the differences could be noise. Fourth, no code is released for the ensemble methods, which makes replication harder.

On the positive side, the authors are clear about the binary-only limitation and do not overclaim elsewhere. The experimental setup—natural imbalance groups plus controlled downsampling—is a reasonable way to separate dataset-specific effects from the imbalance signal. The references are relevant. This is not a sloppy paper; it is an honest benchmark that overshoots in its conclusion.

Who is this for? Researchers working on tabular clustering and representation learning, especially those wanting a quick map of which methods behave well under imbalance. I would bring it to a reading group because it will generate a useful discussion about evaluation standards. I would not cite it as evidence that deep clustering rivals supervised classification; I might cite it as a benchmark if the authors release code and rerun with the fixes. Peer review: this deserves a serious referee, and a good one will send it back for major revision rather than desk reject.

Referee Report

4 major / 5 minor

Summary. The paper evaluates eight clustering methods, including two proposed ensemble approaches, on 16 binary OpenML tabular datasets under natural and artificially induced class imbalance. G-CEALS-EE aggregates G-CEALS cluster assignments across embedding dimensions; KGT applies majority voting to the top three ranked methods (K-means, G-CEALS-EE, TabClustPFN). Performance is measured by ACC, NMI, and ARI. The authors report that the ensemble methods achieve the best average ranks and conclude that deep clustering can serve as a strong alternative to supervised classification in imbalanced settings.

Significance. If the empirical claims hold, the paper provides a useful comparative benchmark of deep clustering methods on tabular data under imbalance, and the two ensemble strategies are simple, generalizable ideas. The study is also honest in reporting mixed per-method strengths (e.g., TabClustPFN's high NMI but poor ACC). However, the headline claim about being a 'strong alternative to supervised classification' is not supported by the experimental design, and the KGT ensemble's composition is selected post hoc on the evaluation data. The paper would be strengthened by adding supervised baselines, a proper validation-based selection protocol, and statistical significance testing.

major comments (4)

[Abstract and Section 5/6] The abstract and conclusion state that deep clustering 'can serve as a strong alternative to supervised classification' in imbalanced settings, but no supervised classifier is evaluated anywhere in the paper. All eight compared methods are unsupervised. To support this comparative claim, the experiments must include standard supervised baselines (e.g., logistic regression with class weights, XGBoost) on the same 16 datasets and the same imbalance ratios, reporting ACC, NMI, and ARI (or at least ACC) against the same ground-truth labels. Without such a comparison, the paper only supports the weaker claim that ensembles outperform individual clustering methods.
[§4.1, §5.2, Table 2] KGT's membership is selected by ranking the individual clustering methods on the same 16 datasets used for evaluation ('we rank the clustering methods by overall performance and then obtain an ensemble of inferences from the top three methods'). Since K-means, G-CEALS-EE, and TabClustPFN are chosen after seeing their performance on these datasets, the reported KGT advantage is at least partly self-confirming and may not generalize. The authors should select ensemble members on a validation split (or prespecify them based on published benchmarks) and then evaluate on held-out test datasets, or otherwise demonstrate that KGT's superiority is robust to member selection.
[§5.2 and Figure 1] The reported scores are min–max normalized per dataset across methods before averaging, and the paper provides no statistical significance tests. The parentheses in Table 2 appear to be standard deviations, but no pairwise comparisons (e.g., Friedman test with Nemenyi post-hoc, or Wilcoxon signed-rank with multiple-testing correction) are reported. The claim that 'ensemble methods achieve the best performance' is based solely on average ranks. Adding significance tests and reporting raw (unnormalized) average scores would make the comparison more robust and interpretable.
[§4.4] The definition of ACC in Eq. (4) is written as a maximum over a function n(·) but the notation is incomplete: it suggests the Hungarian optimal mapping, yet the equation as typeset is not a well-defined optimization. The text explains the Hungarian method, but the formal notation should be corrected to make the optimization explicit. This is a minor technical issue but affects a core evaluation metric.

minor comments (5)

[Eq. (1)] The 50% label-match threshold in Eq. (1) is arbitrary and not justified. A brief discussion of its sensitivity (e.g., results with thresholds 40% and 60%) would strengthen the ensemble method.
[§5.1] The embedding dimension sweep is described as 'between 2 and min(33, D) in increments of 3'; please clarify whether the grid is {2,5,8,...,32} or includes other values, and how Rmax=33 was chosen.
[Table 1] The class ratio column mixes notations such as '731:1.1' and '91:1.9'; this is confusing because the majority/minority ordering is not consistent. Use a single convention (e.g., majority:minority) throughout.
[General] No code or configuration details are provided for reproducing the experiments. Since the paper is empirical, releasing code and hyperparameter settings would substantially improve reproducibility.
[Section 6] The limitation that the ensembles handle only binary clustering is stated, but the paper does not discuss how the label-alignment step (Eq. 1) would be extended to multiple classes. This is a known limitation, not a blocker, but a sentence on a potential multi-class alignment strategy would be helpful.

Circularity Check

1 steps flagged · score 4.0 of 10

KGT ensemble members are selected post hoc on the same evaluation set; otherwise the empirical comparisons are not circular.

fitted input called prediction [Section 4.1 (Proposed ensemble method), Section 4.3, Table 2, Section 5.2]
"Second, we rank the clustering methods by overall performance and then obtain an ensemble of inferences from the top three methods using majority voting. ... KGT is the majority-voting ensemble of K-means, G-CEALS-EE, and TabClustPFN."

The ranking used to select the KGT members is computed on the same 16 datasets and the same ACC/NMI/ARI metrics that are later reported as the evaluation (Table 2). No validation or held-out split is described for member selection. Choosing the top three methods by their performance on the benchmark and then reporting that KGT outperforms individual methods makes the comparison partially self-confirming: the ensemble's membership was fit to the very evaluation it is judged on. The voting itself is a genuine aggregation, so the circularity is partial rather than total, but KGT's 'best ensemble' status is inflated by construction.

full rationale

The paper is an empirical benchmark rather than a derivation, and most of its comparisons are not circular. G-CEALS-EE is a genuine aggregation of G-CEALS runs across embedding dimensions with a label-alignment rule; it does not reduce by definition to its inputs. The self-citation [18] (Samad, Hou, and Ghosh) is used only to motivate the embedding-dimension schedule and is not load-bearing for the central claim. The one identifiable circular element is the KGT ensemble: its members are selected by ranking methods on the same 16 datasets and metrics used for evaluation, so the claim that KGT is among the best is partly a consequence of selection on the test bed. The additional concern that no supervised baseline appears, despite the abstract's claim that deep clustering is a 'strong alternative to supervised classification,' is a correctness/scope risk rather than circularity. Overall score 4 reflects this partial, localized circularity in the KGT comparison, while the rest of the paper's empirical content remains independent.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper's central claims rest on several hand-chosen design parameters: the 0.5 threshold, the reference embedding dimension, the embedding sweep schedule, and especially the post-hoc selection of KGT members from the evaluation datasets. No new physical or conceptual entities are introduced. The benchmark, not any derived quantity, is the main output, so the axiom burden is moderate.

free parameters (5)

Ensemble threshold = 0.5
Equation 3 thresholds Y_avg at 0.5 to obtain Y_ens; hand-chosen, not justified.
Reference embedding dimension = r_j = 2
Section 4.1 uses r_j=2 as the reference embedding for label alignment; arbitrary.
Embedding dimension sweep = 2 to min(33, D) in steps of 3
Section 5 states the G-CEALS-EE dimension is varied from 2 to min(33,D) in increments of 3, 'following a similar approach [18]' (authors' own prior work).
KGT ensemble membership = K-means, G-CEALS-EE, TabClustPFN
Section 4.1 selects the top three methods by overall performance on the same 16 datasets used for evaluation — post-hoc selection.
Imbalance group bins = CR<2, 2–3, 3–7, 7–9, >=9
Section 4.2 defines five class-ratio groups using hand-chosen thresholds.

assumptions (4)

domain assumption Binary cluster labels from different embedding runs can be aligned by majority agreement with a fixed reference embedding
Equation 1 assumes flipping labels when agreement with r_j=2 is below 50% yields concordant assignments; no proof that reference-based alignment preserves cluster structure.
domain assumption Min-max normalization across methods per dataset is an appropriate basis for comparing clustering performance
Table 2 and Figure 1 use per-dataset min-max normalization, converting absolute metric values into relative ranks; this underpins the 'outperform' conclusion.
domain assumption Ground-truth labels are used only for evaluation, not training or hyperparameter optimization
Section 4.4 states this, but the text does not provide code or logs to verify it for all baselines.
standard math Hungarian algorithm and standard metric definitions (ACC, NMI, ARI)
Equations 4–6 rely on standard, well-known definitions; no new mathematical claims.

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Cite this review

Pith. "Pith review of Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data." pith.science (2026). https://pith.science/paper/SJS73M6Z

@misc{pith2026260800346,
  author       = {Pith},
  title        = {Pith review of: Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SJS73M6Z}},
  note         = {Machine review of arXiv:2608.00346}
}

read the original abstract

Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy. Unsupervised deep clustering can be immune to class imbalance because representation learning for clustering is performed without class labels. Deep clustering has been proposed for images, languages, and graphs, while its application to tabular data has only emerged recently. This paper is among the first to examine the performance of state-of-the-art deep clustering methods under varying levels of data imbalance. We introduce two novel cluster ensemble approaches: one aggregates deep clustering assignments across different embedding dimensions, and the other applies majority voting to the best-performing clustering algorithms. Experiments on 16 binary tabular datasets with varying and artificially induced levels of imbalance reveal distinct strengths of different deep clustering methods. On average, our ensemble methods outperform individual clustering methods in ACC, NMI, and ARI scores, offering greater resilience to data imbalance when identifying ground-truth classes without supervision. Therefore, in an imbalanced data scenario, deep clustering can serve as a strong alternative to supervised classification.

Figures

Figures reproduced from arXiv: 2608.00346 by the authors.

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Reference graph

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Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [1]

Caron, P

M. Caron, P. Bojanowski, A. Joulin, M. Douze, Deep clustering for unsupervised learning of visual features, in: Proceedings of the European conference on computer vision (ECCV), 2018, pp. 132–149

2018

[2] [2]

Q. Xu, H. Gu, S. Ji, Text clustering based on pre-trained models and autoencoders, Frontiers in Computational Neuroscience 17 (2024) 1334436

2024

[3] [3]

Y . Liu, J. Xia, B. Wu, S. Zhou, X. Yang, K. Liang, C. Fan, Y . Zhuang, G. Yu, S. Z. Li, et al., A survey of deep graph clustering: Taxonomy, challenge, application, and open resource, IEEE Transactions on Knowledge and Data Engineering

[4] [4]

Abrar, A

S. Abrar, A. Sekmen, M. D. Samad, Effectiveness of deep image embedding clustering methods on tabular data, in: 2023 15th International Conference on Advanced Computational Intelligence (ICACI), IEEE, 2023, pp. 1–7

2023

[5] [5]

J. Xie, R. Girshick, A. Farhadi, Unsupervised deep embedding for clustering analysis, in: International confer- ence on machine learning, PMLR, 2016, pp. 478–487

2016

[6] [6]

X. Guo, L. Gao, X. Liu, J. Yin, Improved deep embedded clustering with local structure preservation., in: Ijcai, V ol. 17, 2017, pp. 1753–1759

2017

[7] [7]

Boubekki, M

A. Boubekki, M. Kampffmeyer, U. Brefeld, R. Jenssen, Joint optimization of an autoencoder for clustering and embedding, Machine learning 110 (7) (2021) 1901–1937. 7 APREPRINT- AUGUST4, 2026

2021

[8] [8]

Ghasedi Dizaji, A

K. Ghasedi Dizaji, A. Herandi, C. Deng, W. Cai, H. Huang, Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 5736–5745

2017