PONE-D-25-43831H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionalityPLOS ONE

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PLOS ONE

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Additional Editor Comments (if provided):

Reviewer #1:

Reviewer #2:

Reviewer #3:

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This manuscript introduces H-NGPCA, a hierarchical clustering algorithm for data streams that adaptively determines both the number of clusters and their local dimensionality. The method combines centroid-based (Neural Gas), model-based (PCA), and hierarchical clustering in a fully online manner. The work is technically sound, well-motivated, and addresses an important challenge in streaming data analysis. The experimental evaluation is thorough, and the visualizations effectively illustrate the algorithm’s behavior. However, several aspects require clarification and improvement to strengthen the contribution and ensure reproducibility.

The pseudo-code in Appendix B (Algorithm 2) is a valuable addition, but the main text does not sufficiently explain the flow of the algorithm. For example, the interaction between the tree traversal (line 6–16) and the update steps (line 17–27) is unclear. A high-level summary in Section 3.1 would help readers understand the end-to-end process before diving into details.

The authors rightly note that many online algorithms lack implementations, so they compare against offline methods. However, this puts H-NGPCA at a disadvantage. To fairness, consider including a streaming variant of a classic method (e.g., Streaming K-Means) even if not adaptive. In addition, I suggest that the author discuss more about the application of the method. https://doi.org/10.1016/j.compbiomed.2023.107244 DOI: 10.1109/ACCESS.2020.2970838

Table 1 and 2 show that H-NGPCA performs well, but the standard deviations are missing. Including these would help assess the stability of the results across multiple runs.

The claim that H-NGPCA outperforms BIRCH and Affinity Propagation in some cases is supported, but the discussion should emphasize that these are offline methods. The online capability of H-NGPCA is a significant advantage that should be highlighted more clearly.

The complexity analysis in Appendix E.3 is detailed and correct. The per-data-point complexity does not account for the dimensionality adjustment (Algorithm 1), which involves Gram-Schmidt and eigenvalue regression. This should be included in the analysis.

The authors acknowledge that H-NGPCA cannot handle three collinear clusters (Fig. 12) and lacks a merge mechanism. These are significant limitations. A brief discussion of how a merge mechanism could be integrated (e.g., via a quality measure pruning branches) would strengthen the paper.

The algorithm currently uses five hyperparameters. While this is fewer than many streaming algorithms, a table showing the values used for each dataset (or a justification for fixed values) would aid reproducibility.

Reviewer #2: The paper is well-structured and clearly written, presenting a timely and relevant study on H-NGPCA. The hierarchical extension is explained coherently and supported by appropriate experimental validation. Here are some suggestions to further enhance the paper.

Abstract

•The abstract could be strengthened by more explicitly highlighting the novelty of the proposed method. Additionally, presenting key quantitative results would give readers a clearer and more concrete understanding of the contribution and its significance.

Introduction

•The introduction clearly outlines the problem statement; however, the review of related work is somewhat limited. A more thorough discussion of existing dimensionality reduction techniques (e.g., Kernel PCA, Autoencoders, Deep Learning–based methods, t-SNE, UMAP) is recommended. Furthermore, the manuscript should more explicitly articulate the advancement of H-NGPCA over conventional NGPCA, providing a stronger justification for the incorporation of the hierarchical structure.

•A comparison of these dimensionality reduction techniques, along with a clear justification of their relevance, would strengthen the discussion.

•To further strengthen the problem statement, it is advisable to include supporting references that validate and contextualize the identified research gap.

Methodology

•The methodological description is detailed and logically presented, with adequate mathematical formalization.

•Parameter choices, computational aspects, and algorithmic flow are well explained, ensuring reproducibility.

Results and Discussion

•The experimental evaluation is comprehensive, demonstrating clear improvements over conventional PCA and related methods.

•Results are supported by appropriate benchmarking and statistical validation.

Conclusion

•The conclusion effectively summarizes the contributions and highlights the practical implications of the work.

•Future directions are appropriately suggested, adding value to the study.

Reviewer #3: This paper proposes a new clustering algorithm, H-NGPCA, for data streams. It combines three ideas: hierarchical structure, centroid-based clustering algorithm (NG), and online principal component analysis within each cluster/unit. The proposed algorithm is parameter-free in clustering, meaning it can adaptively decide the number of clusters and the number of components in each cluster. Experimental results demonstrate its advantages over the state-of-the-art cluserting algorithm.

Overall, this paper is well presented. However, I have the some comments for improvement in the attached pdf file.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: review-PONE-D-25-43831.pdf

H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality

PONE-D-25-43831R1

Dear Dr. Migenda,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Muhammad Ahsan, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: All comments has been address. So, I recommend this paper publish in plos one as current version.

Reviewer #2: (No Response)

Reviewer #3: Good to see that the authors have incorporated the suggestions from me and the other two reviewers. It looks good to be published for me.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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