基于项集优化重组的频繁项集发现算法  

Algorithm for discovering frequent item sets based on optimized and regrouped item sets

作　　者：王明[1,2] 宋顺林[1] 

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]镇江市高等专科学校电子信息系,江苏镇江212003

出　　处：《计算机应用》2010年第9期2332-2334,共3页journal of Computer Applications

基　　金：江苏省产业信息化重点基金资助项目(1633000004)

摘　　要：发现频繁项集是关联规则挖掘的主要途径,也是关联规则挖掘算法研究的重点。关联规则挖掘的经典Apriori算法及其改进算法大致可以归为基于SQL和基于内存两类。为了提高挖掘效率,在仔细分析了基于内存算法存在效率瓶颈的基础上,提出了一种发现频繁项集的改进算法。该算法使用了一种快速产生和验证候选项集的方法,提高了生成项目集的速度。实验结果显示该算法能有效提高挖掘效率。Discovering frequent item sets is the main way of association rules mining, and it is also the focus of the study in algorithms for association rules mining. The classical Apriori algorithm and its improved algorithms of association rules mining can be generally classified as one based on SQL and the other based on memory. To improve the data-mining efficiency, the authors proposed an efficient algorithm for discovering frequent item sets. After analyzing the efficiency bottlenecks in some algorithms based on memory, the algorithm used a method that could generate and test candidate item sets efficiently to optimize the speed of item sets generation. The experimental results show that the proposed algorithm can assuredly improve the mining efficiency.

关 键 词：数据挖掘 频繁项集 项集数组 逻辑运算 关联规则 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP182[自动化与计算机技术—计算机科学与技术]