Constraint pushing techniques have been developed for mining frequent patterns and association rules. However, multiple constraints cannot be handled with existing techniques in frequent pattern mining. In this paper, a new algorithm MCFMC (mining complete set of frequent itemsets with multiple constraints) is introduced. The algorithm takes advantage of the fact that a convertible constraint can be pushed into mining algorithm to reduce mining research spaces. By using a sample database, the algorithm develops techniques which select an optimal method based on a sample database to convert multiple constraints into multiple convertible constraints, disjoined by conjunction and/or, and then partition these constraints into two parts. One part is pushed deep inside the mining process to reduce the research spaces for frequent itemsets, the other part that cannot be pushed in algorithm is used to filter the complete set of frequent itemsets and get the final result. Results from our detailed experiment show the feasibility and effectiveness of the algorithm.
