Clustering Based on Continuous Hopfield Network

Round 1

Reviewer 1 Report

Report on “Clustering Based on Continuous Hopfield Network”:

In this paper, authors reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n × k neurons to solve it. However, the paper presents some inserting results, I do think that it needs a minor revision according to the following comments:

1. The paper still needs to be motivated well. The motivation should be demonstrated with all possible tolls.
2. The paper iv very poor in visualization. Authors presented the numerical results but there are no plots are there. The paper needs to be enriched with some graphical results.
3. Each equation should be followed by “.” or “,”.
4. Some useful details regarding the used software should be added.
5. It will be better if authors add some future point.
6. The limitation of the proposed method/algorithm is missed.
7. How can readers check those results? I think this point need a deep answer.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

The authors propose an integer optimization model for the clustering problem, then they address it by means of a recurrent neural network. An experimental investigation is provided.

The article is interesting though very concise. The authors do not provide sufficient background information and do not discuss the related works in the area. Therefore, I suggest the authors to extend it by providing a more detailed discussion of related works and also by pointing out possible practical applications of their approach. For instance, it should be interesting to discuss how it can be applied in real-world applications such as e.g. those proposed in

- Yeoh, Jia Ming, et al. "A clustering system for dynamic data streams based on metaheuristic optimisation." Mathematics 7.12 (2019): 1229.
- Abdullah, Dahlan, et al. "The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data." Quality & Quantity (2021): 1-9.
- Shaheen, Muhammad, Saif ur Rehman, and Fahad Ghaffar. "Correlation and congruence modulo based clustering technique and its application in energy classification." Sustainable Computing: Informatics and Systems 30 (2021): 100561.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

I am satisfied with this revision.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf