Hi and thank you for this video, Let's say the ward algorithm is completed and using the CCC criterion we find out that K=5. Since ward method acts by merging clusters (starting by assigning one cluster to each observation) then at some point there must have been k-clusters. After K-1 repetitions of merging we are left we 1 cluster, at this point the ward algorithm has done its job and K-mean is initiated. My question is: Does the K-mean algorithm to create the final number of K-clusters takes into accout the K clusters that were created K-1 repetitions before the end of the ward method or does is randomly chooses K observations as seeds for the final K clusters Thank you
![Hierarchical Cluster Analysis [Simply explained]](/img/n.gif)
very easy to understand, thanks for sharing
Hi and thank you for this video,
Let's say the ward algorithm is completed and using the CCC criterion we find out that K=5. Since ward method acts by merging clusters (starting by assigning one cluster to each observation) then at some point there must have been k-clusters.
After K-1 repetitions of merging we are left we 1 cluster, at this point the ward algorithm has done its job and K-mean is initiated.
My question is: Does the K-mean algorithm to create the final number of K-clusters takes into accout the K clusters that were created K-1 repetitions before the end of the ward method or does is randomly chooses K observations as seeds for the final K clusters
Thank you