Ensembling vs clustering in machine learning
Short answer: Ensembling and clustering are completely unrelated techniques.

Ensembling: Combine the strengths of many diverse models. Ensembles generally do not involve training models on separate sets of data -- it's the models themselves that are different. Generally the more diverse the models the better. For example, an ensemble might comprise the following models: a support vector machine (SVM), a random forest, a gradient boosting model (GBM), logistic regression, k-nearest-neighbors, and a neural network. On Kaggle, I have also seen top competitors create ensembles comprising the same type of model (e.g., random forests) but with a variety of tuning parameters (e.g., one with 100 trees, another with 250 trees, etc). Clustering could be one of the models included in an ensemble. Ensembles are very effective (although extremely complex to operationalize) and can generally improve your error about 2-5%.  

Clustering: Divide the data into mutually exclusive groups with the hope that your cluster-specific models can reduce error by specializing, except in the case of fuzzy clustering where the groups need not be mutually exclusive. At financial companies that build credit risk models, these cluster-specialized models are sometimes called "sub-models" in the sense that they predict the same target variable and therefore fit under an abstract/figurative parent model. This makes model risk reporting easier in the sense that it's simpler to refer to model 1 then model 1a, 1b, 1c, 1d. Clustering in this context is generally based on business knowledge and not a formal clustering technique such as k-means or other types of centroid-based clustering. For supervised learning tasks, formal clustering is generally considered a pretty poor technique because measuring the quality of the clusters is subjective. Your clusters can also vary dramatically depending on how you initialize the centers and how many clusters you specify. In practice, clustering is used much less frequently than ensembling.