A one-phase method for mining high utility mobile sequential patterns in mobile commerce environments

Authors:
Bai-En Shie;Ji-Hong Cheng;Kun-Ta Chuang;Vincent S. Tseng
Affiliations:
Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC
Venue:
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Year:
2012

Citing 11
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Abstract

Mobile sequential pattern mining is an emerging topic in data mining fields with wide applications, such as planning mobile commerce environments and managing online shopping websites. However, an important factor, i.e., actual utilities (i.e., profit here) of items, is not considered and thus some valuable patterns cannot be found. Therefore, previous researches [8, 9] addressed the problem of mining high utility mobile sequential patterns (abbreviated as UMSPs). Nevertheless the tree-based algorithms may not perform efficiently since mobile transaction sequences are often too complex to form compress tree structures. A novel algorithm, namely UM-Span (high Utility Mobile Sequential Pattern mining), is proposed for efficiently mining UMSPs in this work. UM-Span finds UMSPs by a projected database based framework. It does not need additional database scans to find actual UMSPs, which is the bottleneck of utility mining. Experimental results show that UM-Span outperforms the state-of-the-art UMSP mining algorithms under various conditions.