Okay, we can do the accumulative clustering in Python. So we need to import this library first. So we need to import this agglomerative clustering from this SK learn cluster. Then after we import this Acoma retrieval clustering, so we need to do something IDs So, each cluster equal aggro, married the cluster. So n cluster is the number of cluster so I use the same about cluster as the previous one. And affinity is the store manager.

So they'll be UCD. And then in cash will be walk in store so we create these huge clusters. time we want to get the labels on a cluster we can do something IDs acquire each cluster dot fakhri d x k data we can print x and we can print labels. So the labels is cluster okay. So I will run this code here agglomerative clustering are defined so these are l must be small letter. So I run this code here okay I run again affinity okay.

Affinity cannot run again Okay. So, this is s data set and this is a cluster. So, the first row is our cluster eight second row is cluster for the third row is our cluster for the buffalo is our cluster for the fifth row is our cluster the sixth row is our customer. So are these other clusters. So, in a coma rated clustering state and about cluster here is 11. So, we this is how the state of nabob cluster for agrability clustering for K means your source data and cluster to get a number of cluster groups who are from