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Article
Peer-Review Record

Density Peak Clustering Algorithm Considering Topological Features

Electronics 2020, 9(3), 459; https://doi.org/10.3390/electronics9030459
by Shuyi Lu 1, Yuanjie Zheng 1,*, Rong Luo 2,*, Weikuan Jia 1,*, Jian Lian 3 and Chengjiang Li 3
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Electronics 2020, 9(3), 459; https://doi.org/10.3390/electronics9030459
Submission received: 18 January 2020 / Revised: 5 March 2020 / Accepted: 6 March 2020 / Published: 8 March 2020
(This article belongs to the Section Computer Science & Engineering)

Round 1

Reviewer 1 Report

Dear Authors;

Thank you for your interesting proposal. The reviewer believes that this manuscript need revision based on the following comments. 

The contribution of this manuscript is not clear, so please add one paragraph in the introduction and explain about the contribution very clear. Please prepare a block diagram about your proposed algorithm in the introduction and explain about the sub-blocks briefly. The main challenge for clustering is robustness (noise vs. signal/abnormal signal), so how you can solve this challenge based on your proposed algorithm? In the formulation of local density (line 112), how you can define sliding surface “x(X)”? In the line 147, how you preset the threshold value? Regarding Figure 4, how the signal compared with the original one? Is the type of signal’s feature being important or not? The results need more extend.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The current paper proposes the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm in the density-based Clustering algorithms, which is a new Clustering method based on density clustering. The algorithm is validated through experimental results.

 

Comments to author:

- Please add more details of the DPCTF algorithm.

- Please add the units of measurement both abscissa and ordinate in all figures.

- The state of the art should be improved with more references based on mathaeuristic search algorithm, maybe the author could add the following publications:

o Combined Model-Free Adaptive Control with Fuzzy Component by Virtual Reference Feedback Tuning for Tower Crane Systems, Procedia Computer Science, vol. 162, pp. 267-274, 2019.

o Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing, Energies, vol. 12, no. 7, pp. 1–19, 2019.

- In section 7 the author could add a paragraph with the advantages and the disadvantages of the proposed method.

- A section with the novelty of the current paper should be added in introduction or in the conclusion section.

- Please add more details regarding the obtained results.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear Authors,

The reviewer appreciates the work done by the authors which provides a revised version of the paper that contains a lot of improvements. Because of this the paper can be accepted in this present form. 

Good-Luck for your future research^^

Author Response

Dear reviewer,

 

Thanks for your works for our manuscript.

We have revised it completely and uploaded.

 

Best regards

 

Weikuan Jia

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