数据挖掘中基于负边界思想的关联规则增量式更新算法

Negative Border Based Algorism for Incremental Updating Association Rules in Data Mining

关联规则的挖掘是数据挖掘领域中的一个重要领域,而如何高效地从更新后的数据库中对已经推导出的关联规则进行更新是具有非常重要的价值的。文章首先分析了现有增量式更新算法的优缺点,然后明确定义了负边界的概念,接着提出了一种基于负边界思想的关联规则增量式更新算法,并详细描述了这一算法的实现原理和实现过程,然后给出程序伪代码,最后通过实验验证了算法的有效性。

Mining association rules in data mining is an important field,and how to update the derived association rules efficiently from the updated database is very important.The article first analyzes the existing advantages and disadvantages of incremental update algorithm,and then defined the concept of negative border.The negative border based algorism for incremental updating association rules is proposed,and a detailed description of the algorithm realize the principle and process,and then give the program pseudo code,and finally verified validity by experiment.