Typically you are determining Variable Importance in a Tree based on the relationship to a Target variable (Role=Target), but the Variable Importance in the Segment Profile node is based on the relationship to the Segment variable (Role=Segment).  If you specify a Target variable in the Decision Tree node and follow the Decision Tree node by a Segment Profile node, the Decision Tree will report Variable Importance based on the relationship to the Target variable while the Segment Profile node ignores the Target variable role and reports Variable Importance based on the relationship to the Segment role.  In the example above, _NODE_ (which stores the terminal node of the Decision Tree for each observation) is assigned the Segment role.  If the Decision Tree has  k-terminal nodes, the Segment variable _NODE_ will have k levels.  The Variable Importance in the Segment Profile node is then based on a fitting a decision tree model to the k-level variable _NODE_  rather than the Target variable itself.       Hope this helps! Doug ... View more

Hi Mohamed, correct me if I am wrong 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 ... View more