Association Rule Mining
Association Rule Mining
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DOI：
10.4018/978-1-59140-557-3.ch012
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Y. Woon
中科院分区：
文献类型：
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作者：
Y. Woon
关联规则挖掘(ARM)关注的是如何将事务数据库中的项分组在一起。它通常被称为市场篮子分析，因为它可以比作对市场中购物者经常放在篮子里的商品的分析。从统计学的角度来看，它是一种半自动的技术，可以发现一组变量之间的相关性。ARM被广泛应用于各种应用中，包括推荐系统(Lawrence，Almasi，Kotlyar，Viveros，&Duri，2001)、促销捆绑(Wang，周和汉，2002)、客户关系管理(CRM)(Elliott，Scionti，&Page，2003)和交叉销售(Brijs，Swinnen，Vanhoof，&Wets，1999)。此外，它的概念还被集成到其他挖掘任务中，例如Web使用挖掘(Woon，Ng，&Lim，2002)、聚类(Yu&Mamoulis，2003)、离群值检测(Woon，Li，Ng，&Lu，2003)和分类(Dong&Li，1999)，以提高效率和效果。客户关系管理极大地得益于ARM，因为它有助于理解客户行为(Elliott等人，2003)。营销经理可以使用产品的关联规则来制定联合营销活动，以获取新客户。ARM在超市产品交叉销售中的应用在许多情况下都得到了成功的尝试(Brijs等人，1999年)。在一项涉及超市产品推荐个性化的特定研究中，ARM已被成功地应用(Lawrence et al.，2001)。与客户细分一起，ARM帮助收入增加了1.8%。在生物学领域，ARM被用于提取关于蛋白质-蛋白质相互作用的新知识(Oyama，Kitano，Satou，&Ito，2002)。它还被成功地应用于基因表达分析，以发现不同基因之间或不同环境条件之间的生物相关性(Creighton&Hanash，2003)。
Association Rule Mining (ARM) is concerned with how items in a transactional database are grouped together. It is commonly known as market basket analysis, because it can be likened to the analysis of items that are frequently put together in a basket by shoppers in a market. From a statistical point of view, it is a semiautomatic technique to discover correlations among a set of variables. ARM is widely used in myriad applications, including recommender systems (Lawrence, Almasi, Kotlyar, Viveros, & Duri, 2001), promotional bundling (Wang, Zhou, & Han, 2002), Customer Relationship Management (CRM) (Elliott, Scionti, & Page, 2003), and cross-selling (Brijs, Swinnen, Vanhoof, & Wets, 1999). In addition, its concepts have also been integrated into other mining tasks, such as Web usage mining (Woon, Ng, & Lim, 2002), clustering (Yiu & Mamoulis, 2003), outlier detection (Woon, Li, Ng, & Lu, 2003), and classification (Dong & Li, 1999), for improved efficiency and effectiveness. CRM benefits greatly from ARM as it helps in the understanding of customer behavior (Elliott et al., 2003). Marketing managers can use association rules of products to develop joint marketing campaigns to acquire new customers. The application of ARM for the cross-selling of supermarket products has been successfully attempted in many cases (Brijs et al., 1999). In one particular study involving the personalization of supermarket product recommendations, ARM has been applied with much success (Lawrence et al., 2001). Together with customer segmentation, ARM helped to increase revenue by 1.8%. In the biology domain, ARM is used to extract novel knowledge on protein-protein interactions (Oyama, Kitano, Satou, & Ito, 2002). It is also successfully applied in gene expression analysis to discover biologically relevant associations between different genes or between different environment conditions (Creighton & Hanash, 2003).