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!
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?
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.
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!
You totally rock, I actully get this now. Thanks for the clear, jargon free explanation!
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.
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 found something easy to understand. Thank You!!!
Superb explanation Aysan! Thank you!
very clear and succinct example, great job
Best. Thanks for your time putting this together 👍🏻
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.
Thanks! very concise and simple explanation.
really, really good stuff. Thank you
Finally i understand k-modes.Thank you
best explanation about k-modes, big respect for you.
keep up the good work !
sorry if my english is bad
Thank you so much for this video!
just perfect!
thanks for turning what seems difficult into easy
Thank you very much!
Do you have any tips on what algorithms are best for clustering of high dimensional binary spars matrix?
Very clear thanks!!
Best explaination so far
Thank you very much for this video, mate!
No worries at all mate
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!
For C3 in the 1st iteration the distance from point 6 should be 3 not 4
Great work❤❤
excellent explanation. Thanks :)
Thank you.
thank you so much !
Thanks mate!
Thanks for this explanation :)
Very nice. thanks.
The only relevant video on K-Mode on YouYube.
You sound like ALI G. BIG UP RESPECT
Thank you so much for this video! Very helpful :)
Aishwarya A R My pleasure!
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?
best video for K-modes
Thanks!
Man, I love you. The Ali G of Statistics.
haha i thought the same thing
Very instructive video!
Thank you!
Thank you
Nice Work Sir
thxs bro
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.
Please ignore if already mentioned but the elbow method can be used to determine K clusters
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?
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.
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?