Predict the customer behavior in the shopping by distributed learning automata

Predict the customer behavior in the shopping is important from two aspects: one is from the perspective of goods suppliers and the other from shop owners. Both groups want to know that their customers interest in which goods and buy which sequence of the goods. In the this paper we provide a way in finding sequences of the customers´ shopping, which in comparison with the previous methods, it works better and we demonstrate that it could obtain sequences of the customers´ shopping in shorter time than the previous methods. Finding of the sequences is very essential for suppliers of goods and shop owners and will lead to an increase in annual profit. In this article, we provide a method of finding two-member and higher sequences by distributed learning automata; its costs is lower than the other methods. We examined it on online basket data of costumer shopping and it is clear that the results are much better.