The choice of distance metric is mainly decided on how you define similarity, which I discuss in this video: ruclips.net/video/uWf__KIKzPQ/видео.html To select linkage function I have previously used some sort of bootstrapping, where you can select the linkage function that generates the most robust clusters, see for example this paper: pubmed.ncbi.nlm.nih.gov/29079427/
Best video I've seen on the subject, well done!
thank you for the crystal clear explanation
Always enjoy watching your videos. Thank you! Waiting for more! 👍 💐
Thank you!
Thank you very much for the great lecture!
Wonderful explanation. Cheers!
Nicely narrated....
Excellent video. Things are explained really clearly!
Thank you!
Thanks for your video, greatly helpful!
Thank you!
Very useful
Love these videos! Is there a way to decide which distance and clustering method works best (i.e. does it depend on the type of data)?
The choice of distance metric is mainly decided on how you define similarity, which I discuss in this video:
ruclips.net/video/uWf__KIKzPQ/видео.html
To select linkage function I have previously used some sort of bootstrapping, where you can select the linkage function that generates the most robust clusters, see for example this paper:
pubmed.ncbi.nlm.nih.gov/29079427/