约束性相联规则发现方法及算法

Algorithms for Mining Constrained Association Rules

文中研究了在大型事务数据库中发现有约束条件的相联规则问题 ,提出了有效实现约束性相联规则发现的两种方法 :过滤数据库算法 Filtering和频繁项集生成算法 Separate.这两种可以同时并用的方法比已有算法运算效率有显著提高 .

The problem of discovering association rules has received considerable research attention and some fast algorithms for mining association rules have been developed. The authors consider the problem of discovering constrained association rules between items in a large database of sales transactions. Instead of applying such constraints as a post processing step, integrating them into the mining algorithm can dramatically reduce the execution time. This paper presents two new algorithms called Filtering and Separate for solving this problem that are greatlly different from the known algorithms. The two proposed algorithms can be used separately and also can be used together. By discussing their tradeoffs, it shows that Filtering can decrease the size of concerned database effectively, and Separate outperforms the known algorithms greatlly in the number of candidates generated.