Take the tour ×  For the task of churn modelling I was
considering:    1. Compute k clusters for the data   2. Build k
models for each cluster individually.  The rationale for that
is,that there is nothing to prove, that the population of
subsribers is homogenous, so its reasonable to assume that data-
generating process may be diffrent for diffrent "groups"  My
question is, is it an appropriate method? Does it violate
anything, or is it considered bad for some reason? If so, why?
If not, would you share some best practices on that issue? And
2nd thing - is it generally better or worse to do preclustering
than model tree (As defined in Witten,Frank -
classification/regression tree with models at the leafs.
Intuitively it seems that decision-tree stage is just another
form of clustering, but idk if it has any advantages over
"normal" clustering.).  share|improve this question add comment
5 Answers  There is a method called clusterwise regression that
solves similar problem (first clusters data and then builts