Genetic Network Programming with Acquisition Mechanisms of Association Rules in Dense Database
Genetic Network Programming with Acquisition Mechanisms of Association Rules in Dense Database
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DOI：
10.20965/jaciii.2006.p0102
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
K. Shimada;K. Hirasawa;Jinglu Hu
中科院分区：
文献类型：
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作者：
K. Shimada;K. Hirasawa;Jinglu Hu
为了提高密集数据库中关联规则提取的性能，提出了一种基于遗传网络规划（GNP）的关联规则挖掘方法。规则提取是在不识别优先级类方法中使用的频繁项集的情况下完成的。关联规则表示为GNP中节点的连接。所提出的机制使用GNP直接从数据库计算关联规则的度量，并通过卡方检验来度量关联的显著性。该系统通过进化方法进行自我进化，并通过遗传操作获得候选关联规则。提取的关联规则被存储在一个池中，通过世代和遗传算子反映为获得的信息。在本文中，我们描述了一种算法，能够发现重要的关联规则，使用GNP与复杂的规则获取机制，并提出了一些实验结果
A method of association rule mining using genetic network programming (GNP) is proposed to improve the performance of association rule extraction from dense database. Rule extraction is done without identifying frequent itemsets used in a priori-like methods. Association rules are represented as the connections of nodes in GNP. The proposed mechanisms calculate measurements of association rules directly from a database using GNP, and measure the significance of the association via the chi-squared test. The proposed system evolves itself by an evolutionary method and obtains candidates of association rules by genetic operations. Extracted association rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe an algorithm capable of finding important association rules using GNP with sophisticated rule acquisition mechanisms and present some experimental results