An evolutionary method for exceptional association rule set discovery from incomplete database
An evolutionary method for exceptional association rule set discovery from incomplete database
复制标题
不完整数据库中异常关联规则集发现的进化方法
DOI：
10.1007/978-3-319-10265-8_12
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Takashi Hanioka
中科院分区：
文献类型：
--
作者：
Kaoru Shimada;Takashi Hanioka
提出了一种从不完备数据库中挖掘异常关联规则集的方法，以发现不完备数据库中有趣的项目组合。规则集定义为每个项目集X、Y分别与类C有弱统计关系或无统计关系，而X和Y的连接与类C有强统计关系。即使数据库中存在缺失值，该方法也可以直接将规则集提取为三个规则的组合。该方法已开发使用的基本结构的进化图为基础的优化技术，并采用一种新的进化策略，通过其进化过程中积累的规则集。该方法利用卡方值实现了不完备数据库中两类之间的关联分析。我们评估了所提出的方法的性能异常关联规则集挖掘不完整的数据库。实验结果表明，该方法具有在医学领域实现基于规则集发现的关联分析的潜力。此外，评估的恶作剧的规则测量缺失值的演示。
A method for exceptional association rule set mining from incomplete database is proposed to discover interesting combination of items in incomplete database. The rule set is defined as each itemset X, Y has weak or no statistical relation to class C, respectively, however, the join of X and Y has strong relation to C. The method extracts the rule set directly as the combination of three rules even though the database has missing values. The method has been developed using a basic structure of an evolutionary graph-based optimization technique and adopting a new evolutionary strategy to accumulate rule sets through its evolutionary process. The method can realize the association analysis between two classes of the incomplete database using chi-square values. We evaluated the performance of the proposed method for exceptional association rule set mining from the incomplete database. The results showed that the method has a potential to realize association analysis in medical field based on the rule set discovery. In addition, the evaluation of the mischief for the rule measurements by missing values is demonstrated.