Reviewer name and names of any other individual's who aided in reviewer	Xianwen Ren
Do you understand and agree to our policy of having open and named reviews, and having your review included with the published manuscript. (If no, please inform the editor that you cannot review this manuscript.)	Yes
Is the language of sufficient quality?	Yes
Please add additional comments on language quality to clarify if needed
Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is?	Yes
Additional Comments
Is the source code available, and has an appropriate Open Source Initiative license <a href="https://opensource.org/licenses" target="_blank">(https://opensource.org/licenses)</a> been assigned to the code?	Yes
Additional Comments
As Open Source Software are there guidelines on how to contribute, report issues or seek support on the code?	Yes
Additional Comments
Is the code executable?	Yes
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Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?	Yes
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Is the documentation provided clear and user friendly?	Yes
Additional Comments
Additional Comments
Is there a clearly-stated list of dependencies, and is the core functionality of the software documented to a satisfactory level?	Yes
Additional Comments
Have any claims of performance been sufficiently tested and compared to other commonly-used packages?	Yes
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Is test data available, either included with the submission or openly available via cited third party sources (e.g. accession numbers, data DOIs)?	Yes
Additional Comments
Are there (ideally real world) examples demonstrating use of the software?	Yes
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Is automated testing used or are there manual steps described so that the functionality of the software can be verified?	No
Additional Comments
Any Additional Overall Comments to the Author	This manuscript presents a clustering algorithm that employs variable neighborhood search and integer linear programming. Benchmark on different datasets confirms the advantage regarding clustering accuracy and computational speed. Overall, it is elegantly designed and well-implemented. A minor error should be corrected before publication. On page 6, cosine is called a distance metric, which is wrong. Cosine is a similarity metric, not a distance metric.
Recommendation	Minor Revisions