Mining big datasets poses a number of challenges which are not easily addressed by traditional mining methods, since both memory and computational requirements are hard to satisfy. One solution for dealing with such requirements is to take advantage of parallel frameworks , such as MapReduce, that allow to make powerful computing and storage units on top of ordinary machines. In this paper, we address the issue of mining closed frequent itemsets (CFI) from big datasets in such environments. We introduce a new parallel algorithm, called CloPN, for CFI mining. One of the worth of cite features of CloPN is that it uses a prime number based approach to transform the data into numerical form, and then to mine closed frequent itemsets by using only multiplication and division operations. We carried out exhaustive experiments over big real world datasets to assess the performance of CloPN. The obtained results highlight that our algorithm is very efficient in CFI mining from large real world datasets with up to 53 million articles.