数据挖掘中一种改进的Apriori算法  

An Improved Apriori Algorithm in Data-mining

作　　者：张昀[1] 

机构地区：[1]中南民族大学计算机科学学院,武汉430074

出　　处：《软件导刊》2009年第11期87-88,共2页Software Guide

摘　　要：关联规则挖掘是数据挖掘领域中的一个非常重要的研究内容,其主要目标就是发现数据库中一组对象之间某种关联。频繁项集挖掘是关联规则挖掘的关键步骤,它在很大程度上决定了关联规则挖掘的效率。介绍了Apriori算法及其算法改进。该改进算法对剪枝步进行了优化,提高了连接效率,并且不断减小数据库的规模,去掉无效事务,减少了每次扫描数据库所花费的时间,提高了算法效率。经过试验论证,性能比原有算法提高,具有一定的实用性。As one of the important contents in data-mining,association rule mining aims to discover the connection or the correlation midst a set of objects in a database. Frequent item set mining is a very important phase of discovering association rules. To some degree, it decides the efficiency of association rule mining algorithm. It also plays an important role in other data-mining tasks such as strong rules, correlation and sequence pattern. This paper generally discusses the data-mining,including the improved algorithm of Apriori. The algorithm can improve the efficiency of the connection, abandon some invalid affairs of the database and reduce the time of database scanning.

关 键 词：数据挖掘 关联规则 频繁项集 APRIORI算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]