FP Growth 算法在购物篮分析研究中的应用.pdf

Transactions on Computer Science and Technology June 2013, Volume 2, Issue 2, PP.24-30 FP-Growth Algorithm for Application in Research of the Basket Analysis 1 2# Qing Tian , Yongmei Liu 1. School of Computer Science, Zhao Qing University, Zhaoqing Guangdong 526061, China 2. College of Information Engineering, Capital Normal University, Beijing 100048, China #Email: snowflash@163.com Abstract During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The FP growth algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or FP tree), the frequent item set is mining by using of FP tree. For the sake of researching the Basket Analysis, the Frequent-Pattern is introduced, Visual C++ is applied to design the program to mine the frequent item sets. In view of the frequent K-item set, the various results are contrasted, the goods which is sell possibly at the same time in the supermarket is arranged in the same place. Keywords: Candidate Sets; Frequent Item Sets; the Basket Analysis; FP-Growth; FP-Tree FP-Growth 算法在购物篮分析研究中的应用* 1 2 田庆 ，刘永梅 1.肇庆学院 计算机学院，广东 肇庆 526061 2.首都师范大学 信息工程学院，北京 100048 摘 要：频繁项集的挖掘过程中，在最小支持度较小的情况下，候选集的产生是算法的主要耗时操作。FP-Growth 算法不 用产生候选集，将提供频繁项集的数据库压缩到一棵频繁模式树 （或FP-Tree），利用FP-Tree 挖掘出频繁项集。为了进 行购物篮分析，根据FP-Growth 算法的理论，运用VC++程序开发工具，对数据进行频繁项集挖掘，针对挖掘得到的频繁 K 项集，对比实验数据指导超级市场将可能同时卖出的商品摆放在一起。 关键词：候选集；频繁项集；购物篮分析；FP-Growth ；FP-Tree 引言 在过去的数十年中，经济