Research on Algorithm for Mining Negative Association Rules Based on Frequent Pattern Tree

作     者：ZHU Yu-quan YANG He-biao SONG Yu-qing XIE Cong-hua 

作者机构：School of Computer Science and Communication Engineering Jiangsu University Zhenjiang 212013 JiangsuChina 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2006年第11卷第1期

页      面：37-41页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：Supported by the National Natural Science Foun-dation of China(70371015) and the Science Foundation of JiangsuUniversity ( 04KJD001) 

主　　题：data mining FP-tree Negative Association Rules 

摘      要：Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items （i. e. absent from transactions）. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, very few algorithms to mine them have been proposed to date. In this paper, an algorithm based on FP tree is presented to discover negative association rules.