The most clear explanation I have found over the internet! Covering a higher difficulty level example and still giving an easy explanation, Please keep making more videos of Machine Learning! Thanks
This is the video I got my concepts cleared about k-modes. However, I would have appreciated If you had added mathematical notation too along the steps. But still, It is a good videos. Cheers!
Dear, When you creating clusters at step 1 and then creating new centroid the value of cluster 1 to 3 are same at row 8. then what is the logic that you assign it to cluster 1 while the value are same on cluster 1,2 and 3. Thanks for such a good tutorial.
This is a great video. Thank you so much. Could you please provide your input on how we can label those clusters? for example, if user1, user2, user3 have watched all movies movie1, movie2, movie3. So this set {movie1,movie2,movie3} can be made like a template because lot of users are watching the same movies. Can we label these groups? If yes, how could we do it? Also, is there a python implementation of your video?
If you are talking about an object having the same distance with all the clusters, then I think you can just assign the object to a cluster at random. At least that is what I have heard other people doing.
I finally understand k-modes! Thank you!
Joseph Wehbe I'm glad it helped!
The most clear explanation I have found over the internet! Covering a higher difficulty level example and still giving an easy explanation, Please keep making more videos of Machine Learning! Thanks
Very intuitive. Thank You. Highly recommended!
This is the best resource on the internet for k-modes. Thank you so much brother
After many videos...This is one of the best on K-Modes...Thanks a lot
Finally i understand k-modes.Thank you
You totally rock, I actully get this now. Thanks for the clear, jargon free explanation!
Finally found something easy to understand. Thank You!!!
Thanks a lot for this wonderful intuition!!! I was feeling confused with K-mode, but after watching this video I understand it better.
Finally I understood what they meant for "dissimilarities"!!! Thank you, great explanation.
Man, I love you. The Ali G of Statistics.
haha i thought the same thing
Best explaination so far
best explanation about k-modes, big respect for you.
keep up the good work !
sorry if my english is bad
Best. Thanks for your time putting this together 👍🏻
Great work❤❤
For C3 in the 1st iteration the distance from point 6 should be 3 not 4
best video for K-modes
thank you very much, this helped a lot as most of the articles use fancy terms and equations but turns out to be a very simple algorithm.
very clear and succinct example, great job
The only relevant video on K-Mode on YouYube.
thanks for turning what seems difficult into easy
Superb explanation Aysan! Thank you!
This is the video I got my concepts cleared about k-modes. However, I would have appreciated If you had added mathematical notation too along the steps. But still, It is a good videos. Cheers!
Thank you very much for this video, mate!
No worries at all mate
Thanks for this explanation :)
Nice Work Sir
Very instructive video!
Thank you!
Thanks! very concise and simple explanation.
You sound like ALI G. BIG UP RESPECT
really, really good stuff. Thank you
Very nice. thanks.
excellent explanation. Thanks :)
Thank you so much for this video!
just perfect!
Thank you so much for this video! Very helpful :)
Aishwarya A R My pleasure!
thank you so much !
Please ignore if already mentioned but the elbow method can be used to determine K clusters
Thank you
Thank you.
Dear,
When you creating clusters at step 1 and then creating new centroid the value of cluster 1 to 3 are same at row 8. then what is the logic that you assign it to cluster 1 while the value are same on cluster 1,2 and 3.
Thanks for such a good tutorial.
Thanks mate!
Very clear thanks!!
Thanks!
Thank you very much!
Do you have any tips on what algorithms are best for clustering of high dimensional binary spars matrix?
Congrats!
This is a great video. Thank you so much.
Could you please provide your input on how we can label those clusters?
for example, if user1, user2, user3 have watched all movies movie1, movie2, movie3. So this set {movie1,movie2,movie3} can be made like a template because lot of users are watching the same movies. Can we label these groups? If yes, how could we do it?
Also, is there a python implementation of your video?
thxs bro
if cluster 1,cluster 2, cluster 3 have same distance, which one we choose? and why?
If you are talking about an object having the same distance with all the clusters, then I think you can just assign the object to a cluster at random. At least that is what I have heard other people doing.
Hi, very clear explanation, thanks.
I have one question, in the case of equal "distance" how do you pick the cluster?
choose randomly
Where do I get more information about k-modes?
Thank you so much. But I still cannot find a good way to determine the k.
I think it is the same way you would for KMeans
What if my data has a mix of categorical and numerical variables? How would the clustering work then?