Mining coverage patterns from transactional databases

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.