Find robust parametric combinations: K-Means

Hi,


I would like to suggest the inclusion of a data mining algorithm called K-Means. The K-Means algorithm is used to make similar groupings in a data set.


From what I have seen optimization already uses genetic algorithms when we have many parameters. The problem then comes in choosing a robust parameter set (having "good neighbors") for a target ratio. Currently we use the 3D map 2 to 2, but if we have many parameters it becomes slow and complicated.


Ideally, search for robust parametric combinations should be added using the K-means Clustering algorithm. And not just one, but several depending on the objective ratios.


For example, software similar to yours already has it implemented:
https://alphadvisor.com/en/user-manual/optimizer-module/


Other references:
https://www.quantnews.com/k-means-clustering-creating-simple-trading-rule-smoother-returns/
https://link.springer.com/chapter/10.1007/978-3-540-72821-4_7
https://medium.com/datadriveninvestor/stock-market-clustering-with-k-means-clustering-in-python-4bf6bd5bd685


I hope you take it into account, it could be a quality leap.


Regards,