Mining Recent High Expected Weighted Itemsets from Uncertain Databases

摘要：

　　Weighted Frequent Itemset Mining (WFIM) has been proposed as an alternative to frequent itemset mining that considers not only the frequency of items but also their relative importance.However,some limitations of WFIM make it unrealistic in many real-world applications.In this paper,we present a new type of knowledge called Recent High Expected Weighted Itemset (RHEWI) to consider the recency,weight and uncertainty of desired patterns,thus more up-to-date and relevant results can be provided to the users.A projection-based algorithm named RHEWI-P is presented to mine RHEWIs based on a novel upper-bound downward closure (UBDC) property.An improved algorithm named RHEWI-PS is further proposed to introduce a sorted upper-bound downward closure (SUBDC) property for pruning unpromising candidates.An experimental evaluation against the state-of-the-art HEWI-Uapriori algorithm is carried on both real-world and synthetic datasets,and the results show that the proposed algorithms are highly efficient and acceptable to mine the required information.

关键词： Weighted frequent itemset Recency constraint Uncertian data Upper bound SUBDC strategy

作者: Wensheng Gan Jerry Chun-Wei Lin Philippe Fournier-Viger Wensheng Gan

作者单位: School of Computer Science and Technology,Shenzhen,China School of Natural Sciences and Humanities Harbin Institute of Technology Shenzhen Graduate School,Sh School of Computer Science and Technology,Shenzhen,China;Department of Computer Science and Informat

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 581-593

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）