一种基于FP-tree的最小预测集获取新算法  

A New Algorithm for Mining the Minimal Prediction Rule Set Based on FP-tree

作　　者：谢翠华[1] 沈洁[1] 李云[1] 程伟[1] 林颖[1] 

机构地区：[1]扬州大学信息工程学院计算机科学与工程系,扬州225009

出　　处：《计算机工程》2006年第6期82-85,共4页Computer Engineering

基　　金：江苏省高校自然科学基金资助项目(02KJB520013)

摘　　要：生成用于预测的关联规则,现有算法生成的关联规则中许多是不必要的。利用分治策略和基于频繁闭子集的FP-TREE生成一种特殊的关联规则集(最小预测集),它比现有规则集小,但是具有同样的预测功能,并且具有发现关键属性的能力。给出了它的算法,并从理论上证明了该算法的正确有效性。Mining transaction database for association rules usually generates a large number of rules ,most of which are unnecessary when used for subsequent prediction and discovery of key data. In this paper, a particular set called the minimal prediction rule set, is mined using the divide-and-conquer strategy and the FP-tree of frequent closet item sets. It is smaller than the association rule set, but has the same ability of prediction and discovery of key data. In this paper, the algorithm of the minimal prediction rule set is given, and its effectiveness is proved in theory.

关 键 词：数据挖掘 关联规则 频繁闭项集 最小预测集 FP—tree 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]