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The authors addressed the remaining issues and therefore I can recomend this article for acceptance.
[# PeerJ Staff Note - this decision was reviewed and approved by Daniel Katz, a 'PeerJ Computer Science' Section Editor covering this Section #]
The author clearly addressed the final requests of the reviewer.
By re-reading the article, however, I notice there are some lacks regarding the internal metrics to assess clustering results. The authors should add some parts regarding the Dunn index and the Gap statistic, plus some information on the metrics to utilize to assess concave clusters (such as DBCV https://doi.org/10.1137/1.9781611973440.96 ).
Reviewer 2 raised some minor issues that need to be addressed before approval.
According to the revised paper, I have appreciated the deep revision of the contents and the present form of this manuscript. But there is still a little content, which need be revised according to the comment of reviewer in order to meet the requirements of publish. A number of concerns listed as follows:
(1) The conclusion and motivation of the work should be added in a clearer way.
(2) How is the complexity of the proposed method? Please describe in detail.
(3) Correct typological mistakes and mathematical errors
no comment
no comment
no comment
The article looks interesting but the reviewers raised a number of issues that need to be addressed before any possible acceptance for publication. I recommend to the authors to take into considerations these drawbacks to prepare a new, improved version of the manuscript.
good paper
very good
it is helpful for clustering community
(1) add more references about density clustering algorithms
(2) add more datasets desicriptions, clustering algorithms are designed for datasets.
my decision is ACCEPT
This survey provides a comprehensive examination of various clustering techniques, including centroid, hierarchical, density-based, distribution and graph based methods, among others. We detail each category methodologies, strengths, and limitations, and explore their practical applications across
multiple domains. The review identiûes and discusses key challenges such as the curse of
dimensionality, initialization sensitivity, and scalability issues, oûering advanced solutions
like dimensionality reduction and ensemble clustering to overcome these obstacles. We
also emphasize the necessity of integrating clustering with other machine learning
paradigms and underscore the importance of robust validation metrics to assess clustering
outcomes eûectively.
no comment
no comment
(1) The abstract should be improved. Your point is your own work that should be further highlighted..
(2) How is the complexity of the proposed method? Please describe in detail.
(3) The values of parameters could be a complicated problem itself, how the authors give the values of parameters in the used methods
(4) In order to highlight the introduction, some latest references should be added to the paper for improving the reviews part and the connection with the literature.
Overall, the article appears to be well-written and structured according to academic standards, with a clear introduction and background, relevant literature references, and a logical structure. Minor improvements could be made in clarity, referencing recent literature, and emphasizing the broader and cross-disciplinary interest of the review.
To enhance the article, ensure alignment with the journal's aims and scope, detail technical and ethical standards, provide exhaustive methodological information for reproducibility, conduct a comprehensive and unbiased survey, verify citations and paraphrasing accuracy, organize content logically, and consider adding visual aids and explicit audience engagement sections.
The article effectively states conclusions that are well-connected to the original research question and limited to supporting results, avoiding unwarranted claims of causation. The argument is well-developed and aligns with the goals set out in the Introduction. However, it could benefit from a clearer identification of unresolved questions, gaps, and future research directions. Consider clearly identifying unresolved questions, gaps, and future research directions in the conclusion to strengthen the article's impact and provide a roadmap for further investigation.
The article could benefit from a more detailed discussion on the practical implications of the findings, ensuring the research's relevance to both the academic community and industry practitioners. Additionally, consider incorporating visual aids such as charts or graphs to enhance the presentation of data and improve overall readability.
The paper is good; however it needs to be modified by addressing the following points:
In the abstract do address the following items:
1. Include more specific examples of applications or algorithms within each category to give readers a clearer idea of the content.
2. Elaborate on what new insights or conclusions the survey provides, emphasizing the potential impact on future research.
3. Ensure that the abstract is concise while covering all necessary points, avoiding overly complex language.
4. Mention any novel contributions or unique perspectives the survey brings to the field.
In the introduction
1. Segregate the contributions from the objectives
2. The guide is missing at the end of the introduction.
The rest of the paper:
1. All equations should be cited properly, including those of not yours.
2. Some of the equations are not numbered at all. Please number them.
3. Also, the survey did not include more recent clustering algorithms such as Improved Evolutionary Clustering Algorithm Star, ECA, ECA, and iECA
As above
As above
As above
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