With categorical data the notion of a "mean" or "centroid" is not so straightforward. You could use the mode (most frequent attribute value) instead of the mean. Or use agglomerative clustering, which does not require you to instantiate centroids.
I've got professor in university who's got PhD in Machine Learning and MS in whatever but he doesn't seem to teach as good as you. Best explanation of k means on internet!. Thank you very much
Thanks. this was very helpful. Glad to have found a video that explained it in a manner that didn't take too long to get the message through. By the by, your voice reminds me of the character Pritchard from Deus Ex: Human Revolution. Sorry if you get that a lot.
I assign attributes all the time in my k-means clusters... for example I will add a male-female categorical. If the data is scaled from 0 to 12, I will assign a 0 to males and 12 to females. For example, if the resulting cluster, gives 6 average then I would say the cluster is equally male and female, a result of 3 would be 'leans male', etc.
Justin Kim Thanks for the kind words. No, the end result will be different each time you run K-means from different starting points. The algorithm finds only a local minimum of the error (intra-cluster variance). If computation time is not an issue, you run the algorithm multiple times, and in the end pick the clustering that gives the lowest intra-cluster variance -- it is, in a way, the best fit of K clusters to your data.
I have rarely seen a clearer presentation.
Thank you!
+Victor Lavrenko
No. Thank YOU
I totally agree!
V10DC86
agree
Thank you! My prof tried to explain this in 1.30h.
You did it in 7 minutes and I understood it even better.
This is because your brain is already primed to learn and knows what it understands and doesn't understand about the topic.
@@induction7895 What are you trying to say?
seriously, best K means explained
So much better than the official university explanation. Thanks a lot for this work!
Awesome explanation. Good and clear introduction with the theory and a perfect example afterwards.
Many thanks to you sir.
finally, someone who explained it step by step and visually, thanks a lot!
Thanks!
Victor Lavrenko if you have categorical data what should you run if not k means??
With categorical data the notion of a "mean" or "centroid" is not so straightforward. You could use the mode (most frequent attribute value) instead of the mean. Or use agglomerative clustering, which does not require you to instantiate centroids.
Fantastically clear and uncomplicated description of k means clustering.
This is the best K-means explanation i've ever seen!
Great job, thank you!
This is the clearest explanation of K-means clustering I have seen. I will be modelling my explanations off of yours. Thank you!
I appreciate the simplicity of the explanation. Very easy to understand. It saved me time.
I've got professor in university who's got PhD in Machine Learning and MS in whatever but he doesn't seem to teach as good as you. Best explanation of k means on internet!. Thank you very much
Best explanation on youtube, don't lose your time looking somewhere else.
Short, Simple, Complete, and to the point. I love it and it helped me understand quickly!
Clear, easy to understand, the last step by step images with the points and triangles really helped.
Really good video, thank you.
Learned from many great resource but this is definitely one of the best open to all explanation of all.
Best 7 minutes spent to understand! Thank you
Best explanation ever. Reminds me of the bubble sorting method, where you sort nearest vector points until no more sorting is needed.
One of the best ever videos on K-means. Please carry on your amazing work .
Thank you, it's very good to know this video was helpful to you.
It's 2021 now, this video is still much much better than my prof's class
1000% clearest k-means explanation out there
This kind of stuff on youtube makes my life so much easier.
One of the best explanations of K-Means clustering. Thanks!
Wow this is Soo well explained... 7years later it's still🔥🔥🔥🔥
Thank you victor, that the best tuto about the K-means I ever watched....
Thanks! Happy to know this is helpful
Your lectures helped me understand many of the concepts I found difficulty with. Thank you!
Thank you! Glad to be able to help.
Clear as much as it can be. It has been useful. Thanks.
Sir, you made it so simple. Thanks. After searching a lot, finally this video made my concepts clear.
I cannot speak such fast and clear at the same time. Nice!
I wish the lecturer i have at uni was as good as you prof Victor.
The example at the end was very helpful, thanks for the video
I can't believe how clear your lecture was! You have helped me SO much!! Many thanks!!!! :D
Great explanation. Just saved myself 20 min figuring out my course notes!
Legendary explanation, with an invaluable example! Thank you Victor
Thank you! It was the best intuitive representation of K-means!
The BEST VIDEO explained K-men EVER ... Thank youuuu
Great and clear explanation. Could you possibly add a video of the formal proof of why this algorithm always converges?
Loved the way your explained the concepts
Thank you. you made it clear with example
Lets cluster out those 108 downvotes
Best explanation ever, hands down. I applaud you good sire.
Excellent .. couldn't have been presented any more clearly. Thank you!
I understand more with you in english language that on my teacher's book in italian. Thank you!
thanks...great explanation (tip: as you said, quick way to visualize starts at 4:50 )!
Just the best explanation heard so far! Good job
Best explanation I've ever come across
Thanks!
Incredible teaching skills!
Whaaaa. I got k-means in 2.5 minutes! This was amazing!
Very succinctly explained. Well done
Clear, brief and excellent. Many thanks VL.
Very clear explanation. Thanks for your work
Excellent explanation, Victor. How lucky must be your students.
Greetings from Mexico
Very good Mr.Lavrenko,
thank you, well done!
This was the best explanation I have ever seen! Thank you so much!!
Awesome video, to the point and explains everything. Just love it.
Fantastic video, broke it down very clearly. Thanks a lot.
The visualizations used are super helpful!
Good that you also describe what Eucalidian means Thank's
this was amazing and blazingly fast ! thanks !
Very clear explanation and presentation. Thanks for you time and effort!
Thank you Graham Norton! You’re the best!
Well explained Victor, thanks for your sincere effort.
Thank you so much for the crystal clear and precise presentation Sir!! :)
Thank you very much, after have found some pages with mathematics formulas, Now I understand the implémentation !
Very happy you find it useful, thanks!
Большое спасибо за понятное объяснение буквально на пальцах
Thank you Victor for your great presentation!
thank you very much sir! much better explanation than I got in class
great explanation, simple and visualized. Thanks! =)
Awesome video. Could you also explain how to decide how many centroids an algorithm should use?
See elbow point in next proposed UA-cam video: ua-cam.com/video/4b5d3muPQmA/v-deo.html
I am basically covering all your classes and making all the notes try my best to make the best of these resources.
Thanks. this was very helpful. Glad to have found a video that explained it in a manner that didn't take too long to get the message through.
By the by, your voice reminds me of the character Pritchard from Deus Ex: Human Revolution. Sorry if you get that a lot.
Wow. Excellent explanation. Thanks.
Seems so simple now, I was getting confused with the recalculation of the centroid location, but thank you :)
very nice explaination ,very clear and concise.thank you very much
hi victor just wanna say thanks coz this really helps me alot , reading text is harder to understand thx for the visuals!
Awesome explanation, thank you Victor!
Thank you Victor verry clear presentation
Incredible Lecture!
That was brilliant, I understood it straight away.
I assign attributes all the time in my k-means clusters... for example I will add a male-female categorical. If the data is scaled from 0 to 12, I will assign a 0 to males and 12 to females. For example, if the resulting cluster, gives 6 average then I would say the cluster is equally male and female, a result of 3 would be 'leans male', etc.
Very clear and concise.
I am so thankful for the time you took to help with clarifying a numerous doubts just in few minutes. You're a wonderful teacher.
Thank you so much for the video. You have made it very clear and simple.
Brilliant video! Many thanks!
This is awesome! Thanks for your explanation!
thank you for you explanation !!! you helped me solve this problem
short, simple and clear
best k-means explanation ever, thanks
one question i have is,
considering the randomly selected centroids,
will the end results always be the same?
Justin Kim Thanks for the kind words.
No, the end result will be different each time you run K-means from different starting points. The algorithm finds only a local minimum of the error (intra-cluster variance).
If computation time is not an issue, you run the algorithm multiple times, and in the end pick the clustering that gives the lowest intra-cluster variance -- it is, in a way, the best fit of K clusters to your data.
Thank you for the help!!! I'm just learning all of this and this video is realy helpful as far as getting a conceptual understanding of all this
Thanks! Really happy to know this is helpful.
Thanks Victor for amazing explanation!
Victor, That was excellent!
Very clear explanation!
I appreciate your explanation! I'll probably be referring to your other algorithm explanations as well
Great diagrams and explanation. Perfect!
very neatly explained!
Thanks for clear explanation!
Very Nicely Explained !!
Very clear illustrations
Excellent explanation..!!!
Stellar explanation. Thanks
What an outstanding presentation 👏👏
Lovely presentation.