MapReduce Clustering for Big Data

We introduce a new clustering procedure specialized for Big Data. It is inspired by the work of [1], and applies a MapReduce procedure for any base clustering algorithm, split-ting the data set at hand, clustering subsamples, and combining intermediate results. We use thus a high level parallelization running a base clustering approach on small samples. We analyse in detail our approach exploring various alternatives and showing its efficiency by simulations.