See all my videos at www.tilestats.... 1. How the method works 2. How to calculate the within-cluster sum of squares (05:37) 3. How to select k based on the elbow method (08:15)
One of the best videos I’ve watched ok K-means and the best explanation of how to use it! Thank you, this really helped me understand the use in conjunction with data. Also a proper explanation of the methods to be used to determine # of centroids needed using the Elbow method. 💪💪💪
best explanation on K Means. I'm not sure why others can't explain it as well when the concept is relatively straightforward, especially with regard to WCSS. I came across multiple medium articles where it made WCSS more confusing.
You are my hero .My major degree is statistics and I believe the theory to understanding these subjects than coding .You're making easy to understanding in complex things in book .I'm so appricate to share your knowledge to us .
00:03 K-means clustering divides data into k clusters. 01:19 Determining the value of k is essential for k-means clustering 02:41 k-means clustering assigns data points to the nearest centroid and updates centroid positions 04:11 Clusters shifting based on proximity to centroids 05:36 Measuring cluster performance with within-cluster sum of squares 06:58 Choosing the best clustering output based on within cluster sum of squares. 08:22 Optimal value of k in k-means clustering 09:39 The optimal number of clusters for the example data is 3.
@TileStats @7:00 what are your values for xhat and Xhat? my BCSS = 2.0 * 7.817901234567899 + 6.0 * 26.382716049382715 + 2.0 * 9.373456790123456 = 192.679012345679 I used as my Xhat the mean of all xhat's, which is [8.5, 7.888888888888889]. my set of xhat's are [9.5, 10.5], [4.5, 4.666666666666667] [11.5, 8.5]. Great Tutorial!
One of the best videos I’ve watched ok K-means and the best explanation of how to use it!
Thank you, this really helped me understand the use in conjunction with data. Also a proper explanation of the methods to be used to determine # of centroids needed using the Elbow method. 💪💪💪
best explanation on K Means. I'm not sure why others can't explain it as well when the concept is relatively straightforward, especially with regard to WCSS. I came across multiple medium articles where it made WCSS more confusing.
You are my hero .My major degree is statistics and I believe the theory to understanding these subjects than coding .You're making easy to understanding in complex things in book .I'm so appricate to share your knowledge to us .
underrated channel, hidden gem
that was great , Thank you for your great explanation
00:03 K-means clustering divides data into k clusters.
01:19 Determining the value of k is essential for k-means clustering
02:41 k-means clustering assigns data points to the nearest centroid and updates centroid positions
04:11 Clusters shifting based on proximity to centroids
05:36 Measuring cluster performance with within-cluster sum of squares
06:58 Choosing the best clustering output based on within cluster sum of squares.
08:22 Optimal value of k in k-means clustering
09:39 The optimal number of clusters for the example data is 3.
However, he already started with k=3
thanks ! keep up the great work!
Thank you!
I have a question, which one should I do first ? elbow method to find the k, or try different starting random centroids for a certain k ?
Elbow last once you have find the best starting pos for the ks.
Great work. One Querstion. How do i calculate the midpoint of the dataset?
Have a look at this video where I calculate the centroid
ruclips.net/video/xXhLvheEF7o/видео.html
Hi sir is k means and kneighborhood algorithms are same ?
No, have a look at this video for KNN
ruclips.net/video/48RqX4HTtCE/видео.html
Like other youtube teachers, failed to explain why k=3 was chosen, why not 2 or four.
It is explained at 8:12 and forward.
@@tilestats Thanks. I see now. I was expecting earlier, and stopped watching (without understanding chosen value of K) the complete video
@TileStats @7:00 what are your values for xhat and Xhat? my BCSS = 2.0 * 7.817901234567899 + 6.0 * 26.382716049382715 + 2.0 * 9.373456790123456 = 192.679012345679
I used as my Xhat the mean of all xhat's, which is [8.5, 7.888888888888889]. my set of xhat's are [9.5, 10.5], [4.5, 4.666666666666667] [11.5, 8.5].
Great Tutorial!
Xhat should be the mean of all data points, or the weighted mean of the three xhats.