Fast Component Density Clustering in Spatial Databases: A Novel Algorithm

Round 1

Reviewer 1 Report

The FCDCSD proposed in this study attempts to solve the current drawbacks of clustering algorithms by utilizing the density-based clustering technique. Experiments show that this method is simple to implement and has fast computing performance, and is not affected by data attributes.

However, there are still the following problems regarding the content of the text：

1. In Creating and using the component in the proposed algorithm, two parameters, Component Size and Component density, are considered, so how to select the appropriate component size (CS) and Minimum density (MD) according to different data sets? Are parameters sensitive?

2. In high-dimensional space, samples have strong sparsity, and the similarity between any two samples is close to zero. Whether the FCDCSD algorithm can reflect the high-dimensional data well?

3. What are the performance and results of the proposed FCDCSD algorithm for the special period when the density difference in the data set is very small?

4. If there are no outliers in the data set, will normal values be misjudged as outliers when the algorithm processes outliers in the later stage?

Author Response

Dear Reviewer,

Thanks for your effort to review the paper. I hope my justifications and revisions for your comments are satisfied you.

Regarding the English language, the paper was sent to a professional proofreader institution before submission.

My regards.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper analyzes different clustering algorithms by utilizing the density-based clustering technique. The drawback of other methods is given. Different quality measures are used.

Good literature review and paper vizualization.

I have only one minor remark:

1) Please add paper contribution into Introduction section.

Author Response

Dear Reviewer,

Thanks for your effort to review the paper. I hope my revisions of your comment satisfied you.

My regards.

Author Response File: Author Response.docx