期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：11
出版社：S&S Publications
摘要：Uncertain data are intrinsic in many real-world applications such as mobile tracking and environmentsurveillance. Mining sequential patterns from imprecise data, such as those data arising from GPS trajectories andsensor readings are important for discovering hidden knowledge in such applications. We establish two uncertainsequence data models abstracted from many real-life applications involving uncertain sequence data, and formulate theproblem of mining probabilistically frequent sequential patterns (or p-FSPs) from data that conform to our models.However, the number of possible worlds is extremely large, which makes the mining prohibitively expensive. Inspiredby the famous systolic tree algorithm, we develop patterns that effectively avoids the problem of “possible worldsexplosion”, and when combined with our pruning and validating methods, achieves even better performance. We alsopropose a fast validating method to further speedup by enabling the pattern within the boundary.
关键词：Frequent patterns; systolic tree; possible world semantics; uncertain databases
