中文摘要：

限制推技术为采矿被开发了经常的模式和协会规则。然而，多重限制不能在经常的模式采矿与存在技术被处理。在这篇论文，新算法 MCFMC (经常的条款的完全的集合与多重限制设置的采矿) 被介绍。算法利用可改变的限制能被推进采矿算法减少采矿研究空格的事实。由使用一个样品数据库，算法开发基于一个样品数据库选择一个最佳的方法把多重限制变换成多重可改变的限制的技术，由连词拆散了或，然后把这些限制划分成二部分。一部分在采矿过程内深被推为经常的条款集合减少研究空格，不能在算法被推的另外的部分被用来过滤经常的 item-setsand 的完全的集合得到最后的结果。从我们的详细实验的结果显示出算法的可行性和有效性。

英文摘要：

Constraint pushing techniques have been developed for mining frequent patterns and association rules. How ever, 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 convert ible 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 experi ment show the feasibility and effectiveness of the algorithm.