Fast FP-Growth for association rule mining  

基于频繁模式树的快速关联规则挖掘算法(英文)

作　　者：杨明[1] 杨萍[2] 吉根林[3] 孙志挥[3] 

机构地区：[1]安徽工程科技学院计算机科学与工程系,芜湖241000 [2]安徽工程科技学院应用数理系,芜湖241000 [3]东南大学计算机科学与工程系,南京210096

出　　处：《Journal of Southeast University(English Edition)》2003年第4期320-323,共4页东南大学学报（英文版）

基　　金：TheNationalNaturalScienceFoundationofChina( 79970 0 92 )andtheNaturalScienceFoundationofAnhuiProvinceofChina ( 0 3 0 42 2 0 5 ) .

摘　　要：In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not construct conditional pattern bases and sub-FP-trees,thus, saving a substantial amount of time and space, and the FP-tree created by it is much smallerthan that created by TD-FP-Growth, hence improving efficiency. At the same time, FFP-Growth can beeasily extended for reducing the search space as TD-FP-Growth (M) and TD-FP-Growth (C). Experimentalresults show that the algorithm of this paper is effective and efficient.提出了一种挖掘频繁项目集的有效算法———FFP Growth ,该算法采用自底向上的策略搜索频繁模式树 ,但不同于FP Growth的是它无须生成条件模式基和频繁模式子树 ,且生成的频繁模式树较TD FP Growth生成的频繁模式树小 ,因而能提高关联规则的挖掘效率 .类似于TD FP Growth的扩展TD FP Growth(M)和TD FP Growth(C) ,FFP Growth很容易被扩展 ,以此来有效地减小搜索空间 .

关 键 词：data mining frequent itemsets association rules frequent pattern tree(FP-tree) 

分 类 号：TP311.131[自动化与计算机技术—计算机软件与理论]