Jul-30-2021, 02:31 AM
I read some online materials on how K-means++ works in selecting initial centroids in KMeans. But I still don't understand.
Eg, if I specify 3 clusters,
1) K-Means++ will select the 1st centroid point (C1) randomly
2) Compute the Euclidean distance of all points from C1.
3) Make the point with the largest distance C2? Then how is C3 found?
Thank you
Eg, if I specify 3 clusters,
1) K-Means++ will select the 1st centroid point (C1) randomly
2) Compute the Euclidean distance of all points from C1.
3) Make the point with the largest distance C2? Then how is C3 found?
Thank you