Critique-Prithu

Hi Jiahong Chen, Thanks for the nice write-up. It covers some seminal works on density-based unsupervised learning. A very fast and useful tool in AI. One thing that bothers me in general, how one can validate the efficacy of different algorithms in unsupervised tasks such as clustering. To illustrate as you say in your page that interleaved cluster are discovered in DBSCAN which might fail in K-means. But an application might not necessarily want that. So it will be good if you can throw some light on that. One more query as it seems that the techniques you described does not support a point being part of more that one cluster. Is that true with any unsupervised clustering method? Just to restress, these are some basic intuition that I feel can help readers to help with the background. Your page does the rest very aptly.

best, Prithu

Hi Prithu,

Thanks for your suggestions! The validation of the efficacy might be carry out with the help of a testing data set with both data points and theirs label. Then just compute theirs correctness. As for another question, I am not sure what other unsupervised learning methods is like, sorry about that. If you have other questions, please let me know.

Best regards, Jiahong Chen