00:01
Now here using the k means clustering we have assumed that we have two dimensional space and four points.
00:09
The points that is given to us is 0 .0, 0 4, sorry, 0 .0, 0 .5, 6 .7 and 7 .0.
00:24
And we want to cluster the example into two groups using the k means algorithm in ukrainian distance.
00:31
So in part a, we have to assume that algorithm is initialized, means 0 .0 and 7 .0.
00:43
So we can use a software and using that we can plot this diagram.
00:48
So this is as follows.
00:50
So using this 4d we can plot our required diagram and here we have a the data point divided on basis of euclidean distance.
01:15
Euclidean distance between 0 .5 and 0 .0 is less than 0 .5 and 7 .0 and same for 6 .7.
01:34
Euclidean distance is less cluster 7 .0.
01:50
Now in b part we have to assume that the cluster is initialized from the means 3 .3 and 7 .7 .0.
01:58
So for that we have...