基于格的快速频繁项集挖掘算法

随着数据库规模的增加或支持度阈值的减少，频繁模式的数量将以指数形式增长，FP-growth算法运行的时空效率将大为降低.本文提出一种基于格的快速频繁项集挖掘算法LFP-growth，算法利用等价关系将原来的搜索空间（格）划分成若干个较小的子空间（子格），通过子格间的迭代分解，将对网格P(I)的频繁项集挖掘转化为对多个子格的并集进行的约束频繁项集挖掘.实验结果和理论分析表明，在挖掘大型数据库时，LFP-growth算法的时间和空间性能均优于FP-growth算法.

Lattice-based Algorithm for Fast Mining Frequent Itemsets

Along with the increasing size of database and the reduction of support threshold, the number of frequent patterns will grow exponentially, and the time and space efficiency of the FP-growth algorithm will greatly reduce. The cause of low efficiency was analyzed, and according to the analysis, a lattice-based algorithm for fast mining frequent itemsets (LFP-growth) was presented. The proposed algorithm divided a large lattice into many sub-lattices by using equivalence relation. Through iterativing decomposition of sublattices, frequent itemset mining in lattice was transformed into frequent itemsets mining in a union set of multiple sublattices. Experiments have shown that the time and space performance of LFP-growth algorithm is superior to that of FP-growth algorithm in mining large database.