一种改进的FP-Growth算法及其在业务关联中的应用

Improved FP-Growth algorithm and its applications in the business association

基于FP-树的FP-Growth算法在挖掘频繁模式过程中需要递归地产生大量的条件FP-树,效率不高,并且不太适合应用在移动通信业务交叉销售等具有业务约束的关联规则挖掘中。因此,提出了基于项目约束的频繁模式树ICFP-树和直接在此树上进行挖掘的新算法——ICFP-Mine。理论分析和实验结果表明,ICFP-Mine算法在内存占用和时间开销等方面比FP-Growth算法更优越,在移动通信业务交叉销售领域的应用中取得了较好的效果。

The FP-Growth algorithm, based on FP-Tree, needs to create a large number of conditional FP-Trees recursively in the process of mining frequent patterns. It is not efficient and not good to apply in mobile communication business cross-selling, in which the association rules mining is business-constraint. Therefore, an items-constraint frequent pattern tree ICFP-Tree and a new ICFP-Mine algorithm which directly mines in the tree were proposed. Theoretical analysis and experimental results show that the ICFP-Mine algorithm is superior to FP-Growth algorithm in memory occupancy and time costs. It has achieved better results in the field of mobile communication business cross-selling applications.