Apriori算法改进及其实现
内容摘要
信息技术的不断推广应用，将企业带入了一个信息爆炸的时代。如何充分利用这些数据信息为企业决策者提供决策支持成为一个十分迫切的又棘手的问题，人们除了利用现有的关系数据库标准查询语句得到一般的直观的信息以外，必须挖掘其内含的、未知的却又实际存在的数据关系。著名的Apriori算法是一种挖掘关联规则的算法。
本文通过对参与候选集的元素计数的方法来减少产生候选集的组合和减少数据库的扫描次数来达到要求。这有利于提高挖掘的速度和减少数据库的I/O 操作时间的开销。
关键字：数据挖掘，关联规则，Apriori算法
Apriori Algorithm And Improved Apriori Algorithm Abstract：An information burst age is coming with the various application of Information technology. How to maximize the information is a very important problem for the decision-maker of the companies. Besides getting the regular information from the Database by SQL-query, people still need to mine the data relation which is unclear but really exists.Association rules is one of the data mining methods, the famous algorithm Apriori is a method, which can be used to solute those problems.
This article analyzes and studies the improved algorithm Apriori based on the algorithm of mining association rules Apriori. The main idea is to decrease the number of candidate items and to decrease the times of Database scanning. The solution is available. It upgrades the speed of data mining and decreases computer's I/O operation. It's proved to be more efficient than the traditional
Key words: Datamining, association rules, Apriori algorithm,
目录
1 数据挖掘.................................................................................................................................. - 1 -
1.1 技术上的定义及含义.................................................................................................. - 1 -
1.2 商业角度的定义.......................................................................................................... - 2 -
1.3 数据挖掘与传统分析方法的区别.............................................................................. - 2 -
1.4数据挖掘不能干什么................................................................................................... - 3 -
2 数据挖掘的几种主要形式:.................................................................................................... -
3 -
2.1:规则挖掘：.................................................................................................................. - 3 -
2.2聚类分析：................................................................................................................... - 4 -
3 关于关联规则的讨论.............................................................................................................. -
4 -
3.1购物篮分析................................................................................................................... - 4 -
3.2 关联规则基本问题描述.............................................................................................. - 4 -
3.3 关联规则挖掘举例...................................................................................................... - 6 -
3.4 关联规则问题的分解.................................................................................................. - 8 -
4 Apriori算法的描述............................................................................................................... - 8 -
4.1 Apriori算法的说明................................................................................................... - 8 -
4.2 Apriori算法的描述................................................................................................... - 9 -
4.3 Apriori算法的举例................................................................................................. - 11 -
5 一种Apriori的改进算法.................................................................................................... - 14 -
5.1算法产生的思路......................................................................................................... - 14 -
5.2算法的图例说明......................................................................................................... - 15 -
5.3本算法的评价:........................................................................................................... - 15 - 附录1 程序运行图示............................................................................................................... - 18 - 附录2 程序代码....................................................................................................................... - 20 -
