Skip Navigation
York U: Redefine the PossibleHOME | Current Students | Faculty & Staff | Research | International
Search »FacultiesLibrariesCampus MapsYork U OrganizationDirectorySite Index
Future Students, Alumni & Visitors
2015 Technical Reports

Memory-Bounded High Utility Sequential Pattern Mining over Data Streams

Morteza Zihayat, Yan Chen and Aijun An

Technical Report EECS-2015-04

York University

October 10 2015


Mining high utility sequential patterns (HUSPs) hasemerged as an important topic in data mining. However,the existing studies on this topic focus on static data and donot consider streaming data. Streaming data are fast changing,continuously generated and unbounded in amount. Suchdata can easily exhaust computer resources (e.g., memory)unless proper resource-aware mining is performed. In thisstudy, we explore a fundamental problem that is how thelimited memory space can be well utilized to produce highquality HUSPs over a data stream. We design an approximationalgorithm, called MAHUSP, that employs memoryadaptive mechanisms to use a bounded portion of memory,to efficiently discover HUSPs over data streams. MAHUSPguarantees that all HUSPs are discovered under certain circumstances.Our experimental study shows that our algorithmcannot only discover HUSPs over data streams efficiently,but also adapt to memory allocation without sacrificingmuch the quality of discovered HUSPs. Furthermore,in order to show the effectiveness and efficiency of MAHUSPin real-life applications, we conduct an analysis on a webclickstream dataset obtained from a Canadian news portal.The results show that MAHUSP effectively discovers usefulpatterns that showcases users? reading behavior.

Download paper in PDF format.

The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.