中文摘要：

目前已提出了许多基于Apriori算法思想的频繁项目集挖掘算法,这些算法可以有效地挖掘出事务数据库中的短频繁项目集,但对于长频繁项目集的挖掘而言,其性能将明显下降.为此,提出了一种频繁闭项目集挖掘算法MFCIA,该算法可以有效地挖掘出事务数据库中所有的频繁项目集,并对其更新问题进行了研究,提出了一种相应的频繁闭项目集增量式更新算法UMFCIA,该算法将充分利用先前的挖掘结果来节省发现新的频繁闭项目集的时间开销.实验结果表明算法MFCIA是有效可行的.

英文摘要：

Mining frequent itemsets is a fundamental and essential problem in data mining application. Most of the proposed mining algorithms are a variant of Apriori. These algorithms show good performance with spare datasets. However, with dense datasets such as telecommunications and medical image data, where there are many long frequent itemsets, the performance of these algorithms degrades incredibly. In order to solve this problem, an efficient algorithm MFCIA and its updating algorithm UMFCIA for mining frequent closed itemsets are proposed. The set of frequent closed itemsets uniquely determines the exact frequency of all frequent itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets, thus lowering the algorithm computation cost. The algorithm UMFCIA makes use of the previous mining results to cut down the cost of finding new frequent closed itemsets. The experiments show that the algorithm MFCIA is efficient.