关联规则挖掘的改进Apriori算法

Yong-qing Wei, Ren-hua Yang, Pei-yu Liu
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引用次数: 28

摘要

Apriori——经典的关联规则挖掘算法是一种从大量的潜在的、规则的知识中发现某些特定知识的方法。但是在数据挖掘过程中存在两个更严重的缺陷。前者需要对业务数据库进行多次扫描,后者不可避免地会产生大量不相关的候选集,严重占用系统资源。在此基础上提出了一种改进的方法。改进算法只对数据库进行一次扫描,同时完成离散数据和相关统计,最后根据最小支持度和频繁项集的特征对候选项集进行剪枝。经过分析,改进后的算法减少了系统资源的占用,提高了效率和质量。
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An improved Apriori algorithm for association rules of mining
Apriori --the classical association rules mining algorithm is a way to find out certain potential, regular knowledge from the massive ones. But there are two more serious defects in the data mining process. The first needs many times to scan the business database and the second will inevitably produce a large number of irrelevant candidate sets which seriously occupy the system resources. An improved method is introduced on the basic of the defects above. The improved algorithm only scans the database once, at the same time the discrete data and statistics related are completed, and the final one is to prune the candidate item sets according to the minimum supporting degree and the character of the frequent item sets. After analysis, the improved algorithm reduces the system resources occupied and improves the efficiency and quality.
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