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In recent years, various algorithms for mining closed frequent itemsets (CFI) have been proposed. Different structures such as prefix sharing are used within these algorithms. However the name challenging problem in many of them is the high requirement of memory, especially in case of sparse datasets. Thus, most of the proposed methods are proper for dense datasets. In this paper we present a new approach to mining closed frequent itemsets using two structures, namely Patricia tree and PTArray. By using these two structures, both the response time and also memory consumption are reduced, significantly. The proposed method, called PTclose is suitable for both dense and sparse datasets. The algorithm is assessed throughout a set of experiments. The results narrate for the relative efficiency of the algorithm compared to other existing methods. [ABSTRACT FROM AUTHOR]