FP-Growth算法在购物篮分析研究中的应用  

FP-Growth Algorithm for Application in Research of the Baske＇t Analysis

作　　者：田庆[1] 刘永梅[2] 

机构地区：[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

出　　处：《计算机科学与技术汇刊（中英文版）》2013年第2期24-30,共7页Transactions on Computer Science and Technology

基　　金：受肇庆市科技创新项目（基金号：2012G26）和肇庆学院自然科学项目（基金号：201212）支持资助.

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。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.

关 键 词：候选集 频繁项集 购物篮分析 FP-GROWTH FP-TREE 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]