基于前缀项集的Apriori算法改进
A Prefixed-Itemset-Based Improvement For Apriori Algorithm
摘要：
关联规则是数据采掘的重要组成部分。它用于从事务数据库中发现有趣的模式。Apriori算法是最经典的关联规则挖掘算法之一，但存在效率瓶颈。提出了一种基于前缀项集的候选项集生成数据结构，该结构提高了经典Apriori算法的效率。
英文摘要：
Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
