Following the raise of ensembling (e.g ensembling of xgboost learners) after its recurrent wins in Kaggle competitions, using it has become a tradition in machine learning. However, some argue that clustering is a form of ensembling where we apply clustering on a data set first, then applying a learning algorithm (to do classification or regression) on each of the resulting clusters. I would like to understand how clustering is different from ensembling?EDITI understand that clustering can be similar to the idea of ensembling if we simply consider homogeneous ensembling where we train the same model on each of the clusters. But I am looking for potential limitations of clustering where using heterogeneous ensembles (different learns for all the data) is the only way to go about the problem.  
Ensembling vs clustering in machine learning
machine learning;clustering;ensemble modeling