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Mining coverage patterns from transactional databases

机译：从事务数据库中挖掘覆盖模式

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摘要

We propose a model of coverage patterns (CPs) and approaches for extracting CPs from transactional databases. The model is motivated by the problem of banner advertisement placement in e-commerce web sites. Normally, an advertiser expects that the banner advertisement should be displayed to a certain percentage of web site visitors. On the other hand, to generate more revenue for a given web site, the publisher makes efforts to meet the coverage demands of multiple advertisers. Informally, a CP is a set of non-overlapping items covered by certain percentage of transactions in a transactional database. The CPs do not satisfy the downward closure property. Efforts are being made in the literature to extract CPs using level-wise pruning approach. In this paper, we propose CP extraction approaches based on pattern growth techniques. Experimental results show that the proposed pattern growth approaches improve the performance over the level-wise pruning approach. The results also show that CPs could be used in meeting the demands of multiple advertisers.
机译：我们提出了一种覆盖模式（CP）模型和从事务数据库中提取CP的方法。该模型受电子商务网站中横幅广告放置问题的启发。通常，广告商期望横幅广告应显示给一定比例的网站访问者。另一方面，为了为给定的网站产生更多的收入，发布者会努力满足多个广告商的报道需求。非正式地，CP是交易数据库中一定比例的交易所覆盖的一组非重叠项目。 CP不满足向下关闭的属性。文献中正在努力使用逐级修剪方法来提取CP。在本文中，我们提出了基于模式增长技术的CP提取方法。实验结果表明，所提出的模式增长方法比逐级修剪方法提高了性能。结果还表明，CP可以用于满足多个广告商的需求。

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