StatQuest: K-means clustering

Please, consider playing the song again, at the end of the video. Thanks.

Just for information,
variation of a cluster = Sigma[i=1 -> number of data-points in a cluster] (distance of data i from the cluster's centroid)^2

The first time in 5 years that i actually understood ML algorithms clearly and actually enjoy them now.

This is brilliantly explained, thank you! It still looks somewhat similar to KNN, confusing to understand which and when to use :/

Hi sir is k means and kneighborhood algorithms are same ?

can anyone please explain what the need is to convert the calculated distance from the last nearest centroid to probability distribution,instead of finding the next centroid just by calculating the distance (in KMeans++)

Great video, but I haven't understood what you mean by variation when explaining how to pick a velue for k, could anyone explain please?

Thanks, Great Video. 👍👍

Nice explanation ❤❤❤

While trying to comprehend K-means Clustering by watching other videos, I struggled to grasp the concept of selecting centroids and assigning them to specific groups. However, this particular video was extremely helpful in clarifying everything for me. I am grateful for the assistance!

Does cluster analysis have to start with a multicollinearity test?

One of the best and simplest explanations of K-means clustering!!

i love it

Great explanation!
Many thanks 👍

Wow, this is K means to the other level.
If you have a video about the 'Distributed Hash Tables" please let me know.

Isn't tsne plot a type of clustering too?

Excellent explanation!

you are Genius:)

How to cluster text data

This is by far tbe best intro oftge series, short , focused and realted

Thank you for my life
- tired student studying for AI final

Is choosing the initial data points randomly, the best option we have? I can't help but think that random would be very inefficient

great

I just watched 3 videos in K means. the first video was very complex and how to resolve a problem in excel but it was too much information to understand K means, then another video with many examples. Your video lecture gives the simple and basics to understand K means clustering concept and guess what? now I feel ready to watch the advance K mean clustering and its application in R and excel. Thanks

Great content and quite simplified well, thanks

This felt like rocket science until today!! thanks!

Great video, Thank you

you explained in 8 minutes what my prof attempted to do in 2 hrs. You are the best!!!!!

what do you mean by variation? do you have a video to explain it?

Sounds like there should be an upgrade to this technique while the location of K points is random only in the first step, after that, there may be used some kind of gradient descent... right?

This is by far the clearest explanation I’ve seen. Great video!

Thank you for explaining this!

Maa saraswati ka ashirwad hai aap par 🙏

you forgot talking about "Outliers"

Loved every min. of video Sir!!
Just studied a day before exam & real glad da’t I did 😌

what a great video

the thumbnail wasn't a clickbait, its really clearly explained!. thank you sir

What if my numeric data is on different scales? Wouldnt that confuse the way it identifies the nearest point? Do I need to scale all my data first?

BEST EVER I CAN FIND ON INTERNET. THANK YOU~

If you ever teach shell scripting you should replace the "bam" with "shabang" or #!

Best Intro ever!

Amazing video. Thank you. Loved it 🙌. At the end, for distances with 3/4 dimensions, shouldn’t those be cube root/fourth root?

This is so wholesome, informative, and engaging all at the same time. Thank you so much for this!

Love the intro tune btw

Bam?!

Great video, thank you so much. Keep it up with the amazing content.

amazing and clear explanation ! horrible song tho

The video was really understandable! But how do you calculate the variation?

You are genius sir! I wish you were my teacher when I was in my graduation.
Thank you 💌

GREAT video! Shame there's cringe at the beginning :D