所以人们开始研究基于数量型的关联规则算法
Tzung-Pei Hong等给出了一种模糊的AprioriTid算法从数量型数据中挖掘模糊关联规则
本文则采用了另一种较为高效的改进Apriori算法：多段支持度算法（MSS）
运用关联规则算法从大量事务中发掘有用的信息和知识正成为一个重要的研究领域
大多数的关联规则算法都是针对布尔型变量的
然而由于现实生活中较为常见的数据都是数量型的
模糊多段支持度算法 Gao Chen * （ Lanzhou Jiaotong University
Apriori算法
并引入了模糊的概念和方法从而提出了模糊MSS算法
该算法进一步的提高了算法的效率
文章的最后给出了一个基于购物篮分析的实例来演示了算法的应用
关键词： 数据挖掘
关联规则
） Abstract： Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area
Most of conventional data mining algorithms identify the relation among transactions with binary values
Transactions with quantitative values are, however, commonly seen in real world applications
Tzung-Pei Hong proposed a fuzzy mining algorithm based on the AprioriTid approach to find fuzzy association rules from given quantitative transactions
This paper proposes another new fuzzy mining algorithm based on the Multi-Segment Support(MSS) approach to to explore interesting knowledge from the transactions with quantitative values
This algorithm is an effective accociation rules
In the end the paper gives an example based on basket data to illustrate the fuzzy MSS algorithm
Keywords： data mining; association rules; MSS algorithm 下载PDF阅读器 PDF全文下载： 初稿 （ 155 ） 作者简介： 通信联系人： 【收录情况】 中国科技论文在线： 高琛
一种改进的模糊关联规则算法[EB/OL]
