In this paper, a frequent itemset mining method is presented. We use cellular learning automata to do fast parallel mining process. The proposed algorithm was tested and the results were compared to SABMA. Experiments are conducted on several experimental data sets with different minsups. Performance study shows that our algorithm outperforms the best former algorithms.