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Hello, Could you suggest any books to thoroughly understand the Unsupervised clustering methods in High Dimensional scenarios? These videos are super helpful, thank you so mucj

Only video in in which why Density Reachable is not symmetric is explained properly! Thanks from India

where or how do you get the intervals? are they given?

well-explained

Very informative

Thank you , the paper explaination helps a lot

Great video but can you please explain how are you calculating diameter in the construction of tree step? 18:57

Can you provide me with this density_based clustering slides? I really need them. Thanks a lot!

Oh my god, I am so glad you put all this stuff up! this stuff is litterally saving my life in my Data mining and Machine Learning class

Understood so well, thank you. And really loved the animation you showed - great for visualization

Finally a good and understandable example. Thank you !

aaammmmmm...

great lecture professor

Thanks

its aoi not oai

Thank you

Thank you very much. My professor didn't explain things as well as you did. Keep up the good work!

Thank you for taking time to post these videos. I found them really useful and helpful. However, I observed that the chapters are not complete. For example, there's no Playlist for chapter 5. Could you share them or provide a link to access them?

Love it !! Thank you sooooooooo much much much much. Love you all, both the instructor and learners who asked questions ^^

I'm from Vietnam and I really appreciate your lecture. What an intuitive lecture!

wasted

Thank you!

Thanks a lot.

Great lecture! Maybe an example about the hash tree will be better!

Thank you! Very useful lecture!

Thanks A Lot! I am New in Data Science Field and your videos helping me.. May God Help You.

Thank you so much! It's a very helpful lecture.

Just a disclaimer, the clustering in the picture in your example has used euclidian distance. Therefor the results in the CF-Tree created in the example does not correspond to what is in the picture. Using the same parameters T=2 and B=3 using euclidian distances results in the clustering circled in the picture.

Thanks a lot for the clear explanation, Professor. It helps my understanding of the topic.

Great lecture. Thanks, Prof.Cui

Make vedio with loud voice and clear hard to listen all above appreciated 👍

This is really good, thank you so much!

崔老师好，终于看到中国老师讲数据挖掘了，感动

(Nice)^4

Great video

You're awesome. Thanks a lot. Keep up. You'll grow a lot

great video.. any body have a link to her course? Please

Thank you for this explanation!

madam the voice in the video is very very low i can hardly hear you.

Finally found an example going over it step by step. Thank you!

(Nice)^3

Nice nice

Nice

Thanks for this wonderful video.

I think there is a minor error in the definition of reachability distance. Shouldn't it be max{core-distance(p), distance(p,q)} rather than max{core-distance(q), distance(p,q)} ?

THANX ALOT

THANX BEST LECTURE

Very informative video!!

Thanks.

this is the best explanation!