Understanding cluster heat maps

[@ChatSceptique]

Best video I've seen on the subject, well done!

[@user-zn7fc6cx7t]

thank you for the crystal clear explanation

[@newsupdates3622]

Always enjoy watching your videos. Thank you! Waiting for more! 👍 💐

[@tilestats]

Thank you!

[@little_pongfi282]

Thank you very much for the great lecture!

[@Hoxgene]

Wonderful explanation. Cheers!

[@Khatrishalu20]

Nicely narrated....

[@nanwu1786]

Excellent video. Things are explained really clearly!

[@tilestats]

Thank you!

[@sxbear9988]

Thanks for your video, greatly helpful!

[@tilestats]

Thank you!

[@geofroykinhoegbe2861]

Very useful

[@brazilfootball]

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)?

[@tilestats]

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/