you saved my life! wish me luck. I'll have an exam in the next 2 days. Hopefully, I can utilize all that you taught in this video. Keep on great work maim!
Thank you Anuradha ji. Finally I understood what is K, what is mean, what is centroid, what is euclidean distance. Please create more videos covering major ML algorithms.
This example is a bit difficult, you can simply take 2 rows directly and group it as 1 & 2, then find the Eucledian distance from each row and the shortest distance is your new row's group from 1 & 2, and the mean will be the grouped rows sum/2. Now the real concept who might go through my comment, K- means clustering is finding the way to group the similar set of data (of any type actually), then why we need a mean here? 1. When you calculate the distance from one point to other point you simply take a-b ( and you know that a>b, however this may not be possible in graphs or 3 dimensional plot, so you take square of sum of the distances for 2 values x,y and then you take a root so that if in a-b a
Formula of calculating Euclidean distance is needs to update as it contains (x-a)+(x-b) but it should be (x-a)+(y-b) also check square of 17, it should be 283 instead of 283.
i think you did it for one iteration only; but in next iteration maybe any point can change its cluster as the two means are changed. So basically we need to reiterate same procedure unless cluster mean value does not change for two consecutive iterations.
Are you sure your values are right ? At 9 min 31 secs the new K1 should be (185+179+182)/3 and (72+68+72)/3 . Also at 10 mins 20 secs k1 = (185 + 179 + 182 + 188)/4 and k2 = (72+68+72+77)/4 final centroid should be 183.5000 72.2500 for K1 and 169.0000 58.0000 for K2. I ran it in Matlab and Matlab confirms these answer.
Hi mam, Good and neat explation for k- means algorithm it was very useful for me I need a explanation of CLARA and CLARANS in partitioning algorithm for my exam ..
Well explained. Just a minor suggestion. Most people watch on mobile so would be good to use entire screen rather than static title on left. I liked vedio
Why you are updating Mean of cluster after every assignment? Aren't we suppose to update the mean after completion of single-one iteration according to the original Algorithm?
I think mean is not calculated every step based on yours and her explaination. First , we assign all the data points to their nearest cluster and then take average of all the points in a cluster as a whole
Wow 💪 Now we will have DWM vidoes. thanks madam. please keep them coming, your content is helping us and yes the BDA paper was lengthy, but your vidoes covered 30 marks or more altogether. Page ranks, sums, FM Algorithm.
Hi mam, i want answer to this question -- Assume you are given n points in a D-dimensional space and a integer k. Describe the k-means ++ algorithm for clustering the points into k cluster
Shouldn't we update the centroid when we find all the distances between every data point and the previous centroid? you updated it with the average of the first two points only, why?
.Thanks for your help .Im appreciate for your time ..But maybe there is a loose in Euclidean Distance [(x,y),(a,b)]=root of(x-a)^2+......(y-b)^2 and not ...(x-b)^2.as my point of view!!
what is the point to calculate distance from centroid 1 to centroid 1 and from centroid 2 to centroid 2? isn't it obvious distance in this case gonna be 0 ?
The centroid coordinates are continuously changing. Initially, we took co-ordinates of points 1 and 2 as two centroids, so should we not re-check if points 1 and 2 still belong to the initial cluster to which they were assigned?
![Hierarchical Agglomerative Clustering [HAC - Complete Link]](http://i.ytimg.com/vi/Cy3ci0Vqs3Y/mqdefault.jpg)
![Hierarchical Agglomerative Clustering [HAC - Complete Link]](/img/tr.png)
![K-Mean Clustering [Single Dataset]](http://i.ytimg.com/vi/rESDfP2ik3k/mqdefault.jpg)
Finally somebody that actually show calculations every step! Thank you so much you have my like!
3 years and this is still very much useful! Thank you so much.
you saved my life! wish me luck. I'll have an exam in the next 2 days. Hopefully, I can utilize all that you taught in this video. Keep on great work maim!
All the best..
OMG, I've been looking for this for so long!!! You are the QUEEN!!!
I Finally understand the math behind this process. Thank you for walking through with actual data. This helps tremendously!
Helped a lot. Can't thank this lady enough. Just a small correction : Distance is (x-a)^2 + (y-b)^2
Thanks.. That's the correction
You made this look so clear and understandable. I sincerely appreciate you for this all-important K-means computation video!
4:53 - 15^2 is 225, not 255
Mam, the explanation was CRYSTAL CLEAR. Thanks! keep making these types of tutorials. It really really helps
Your way of Explanation is easy to grasp Maam, Thank you 😇
Thanks for the informative video ! @3:15, the variable should be 'y', instead of 'x', (y-b)
okay.
The best video seen so far on K-means
Thank you Anuradha ji. Finally I understood what is K, what is mean, what is centroid, what is euclidean distance. Please create more videos covering major ML algorithms.
Your vedio Easily Understand.... Very Nice ma'am
woaaaa really really help me to understand more about K-mean
Really straightforward and easy to understand.
thank you very much, I was really confused how the implementation of this algorithm would be but you made it really easy to understand.
are you mad..??
This example is a bit difficult, you can simply take 2 rows directly and group it as 1 & 2, then find the Eucledian distance from each row and the shortest distance is your new row's group from 1 & 2, and the mean will be the grouped rows sum/2.
Now the real concept who might go through my comment, K- means clustering is finding the way to group the similar set of data (of any type actually), then why we need a mean here?
1. When you calculate the distance from one point to other point you simply take a-b ( and you know that a>b, however this may not be possible in graphs or 3 dimensional plot, so you take square of sum of the distances for 2 values x,y and then you take a root so that if in a-b a
Really Excellent Mam...
Formula of calculating Euclidean distance is needs to update as it contains (x-a)+(x-b) but it should be (x-a)+(y-b) also check square of 17, it should be 283 instead of 283.
Excellent Video....Thanka lot mam...you saved my time
Thanks for nice explanation, it helps.
Very very useful 👍 Thank u so much......💫
very very useful video thank you so much madam.
Thanks mam 👍🏼Jazak Allah mam
i think you did it for one iteration only; but in next iteration maybe any point can change its cluster as the two means are changed.
So basically we need to reiterate same procedure unless cluster mean value does not change for two consecutive iterations.
Prasad Nagarale agreed. I have one question.which centroid values we should consider for next iteration..
a big thanks to you for this wonderful explanation
Thanks for the amazing job.
Really simple and clear to understand, congratulations!
Thank you, Anuradha for such a comprehensive example.
Anurag Yadav thank you so much
Anuradha great work. No where I got this detailed explanation.Please try to do videos for deep learning algorithms in a detailed way like this.
awesome explanation
Thanks a lot. Systematic explanation and crystal clear.
Great stuff, thanks for explaining.
very clear explanation!
Thanks for being my Savior for 10 marks.
Are you sure your values are right ? At 9 min 31 secs the new K1 should be (185+179+182)/3 and (72+68+72)/3 . Also at 10 mins 20 secs k1 = (185 + 179 + 182 + 188)/4 and k2 = (72+68+72+77)/4 final centroid should be 183.5000 72.2500 for K1 and 169.0000 58.0000 for K2. I ran it in Matlab and Matlab confirms these answer.
Thank you so much mam,, love 😍 ❤️
Dear Anuradha, thank you so so much.
Hi mam,
Good and neat explation for k- means algorithm it was very useful for me
I need a explanation of CLARA and CLARANS in partitioning algorithm for my exam ..
very good session...but there should be X-a and Y-b.
Best one from all others👏
great madam
Well explained. Just a minor suggestion. Most people watch on mobile so would be good to use entire screen rather than static title on left. I liked vedio
Ashutosha Nadkarni thanks
Thank you mam for detailed explanation
Great explanation for K-means!
Thanks.
Thank you so much ma'am for amazing explanation!
Can we taken randomly any two initial centroids ?
great and easily understandable explanation.
M Anbazhagan thanks
In the next dataset means 3rd one the value should be 17 square should be 289 it is 283 Ik the answer is correct just for informing
Thanks a lot 🥰🥰🥰🥰
Why you are updating Mean of cluster after every assignment? Aren't we suppose to update the mean after completion of single-one iteration according to the original Algorithm?
Ma'am, how do we know that we have assigned the initial 2 items in the right clusters?
ar 3:50 its y-b ( euclidean distance) and not x-b
mean is calculated in a wrong manner ,we have to take avg of all value in our set when ever some new value is added..
you are correct
I think mean is not calculated every step based on yours and her explaination. First , we assign all the data points to their nearest cluster and then take average of all the points in a cluster as a whole
Fully explained
Wow 💪 Now we will have DWM vidoes. thanks madam. please keep them coming, your content is helping us and yes the BDA paper was lengthy, but your vidoes covered 30 marks or more altogether. Page ranks, sums, FM Algorithm.
Thanks,
Will surely put .
Uploaded, Hierarchical Agglomerative Clustering, and Apriori Algorithm.
Anuradha Bhatia yes madam, I got the notification :) thanks.
More following.
Anuradha Bhatia That's great. Waiting eagerly :)
Love's urs lecture
thanks maam.
Wonderful lecture mam.. thank you
Thanks
thanks mam for explanation
cluster assignment for the first 2 clusters is an assumption though we can justify it by euclidean distance calculation
Great explanation madam
Thank you😊
Thanks a lot, ma'am. Helped me for my exam!
what is the point of finding the distance between the two initial clusters?
the pts themselves are the centroids for their respective cluster, right?
Hi mam,
i want answer to this question -- Assume you are given n points in a D-dimensional space and a integer k. Describe the k-means ++ algorithm for clustering the points into k cluster
Don't update cluster centroid after every assignment update it after a whole iteration (all assignments in one iteration is complete)
madam can you put more topics in data warehouse on youtube .
Very well explained😘
Thanks
thank you
why u didn't do update means as you did in single dataset video?
Shouldn't we update the centroid when we find all the distances between every data point and the previous centroid? you updated it with the average of the first two points only, why?
www.datacamp.com/community/tutorials/k-means-clustering-python
exactly
Well explained mam tq
Thanks for the explanation.. it's is very clear...
4:23 wrong formula, you forgot the y and replaced it by x
Thank you 👏👏👏🙏👼
Superb explanation. Thank u
Great! Thank you for the video!
Thank you mam
How can we find the K value for a large data set?
thanks for everything this video is very instructive...
İSMAİL KAR NANOKUNG Thank You
Wonderful lecture Mam... For more videos, the link is not working... Kindly post more videos on Datamining. Thank you.
What was the relevance of (0,21.93) values. There was no point of calculating that.
Thank you so much ma. Very helpful video.
.Thanks for your help .Im appreciate for your time ..But maybe there is a loose in Euclidean Distance [(x,y),(a,b)]=root of(x-a)^2+......(y-b)^2 and not ...(x-b)^2.as my point of view!!
what is the point to calculate distance from centroid 1 to centroid 1 and from centroid 2 to centroid 2? isn't it obvious distance in this case gonna be 0 ?
Best explanation ever about k-means!
Thanks!!
Ma'am in which cluster should we assign the coordinate(or data point) if the euclidean distance is the same from both the clusters?
You can assign it in any of the 2 in that case
how can i have this slides
Mem Cosine Similarity Ka Sum Ka Examples How to find.... Ek Vedio Bnaake Rakho.... Plz
15 square is 225. Not 255. Please focus on small mistakes. Mean calculation is also wrong.
Ich schick dich gleich ins Vakuum!
excelente canal muchas gracias por tu conocimiento , like y suscripcion.
simply wonderful/easy to understand
thank you for good and detailed explained
Please how can one select cluster head after getting the nodes into clusters. Please help me out
have to select random ones . better select any initial one '
@@fardeen2158 please help me out properly, I don't understand selecting the initial one please
thanku
Perfectly clear, thank you very much.
@Anuradha Bhatia can you help me to apply K-Mean Clustering in Localization using VLC
The centroid coordinates are continuously changing. Initially, we took co-ordinates of points 1 and 2 as two centroids, so should we not re-check if points 1 and 2 still belong to the initial cluster to which they were assigned?