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

Bai En Shie, Ji Hong Cheng, Kun-Ta Chuang, Vincent S. Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings
Pages616-626
Number of pages11
DOIs
Publication statusPublished - 2012 Aug 1
Event25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012 - Dalian, China
Duration: 2012 Jun 92012 Jun 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7345 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012
CountryChina
CityDalian
Period12-06-0912-06-12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Shie, B. E., Cheng, J. H., Chuang, K-T., & Tseng, V. S. (2012). A one-phase method for mining high utility mobile sequential patterns in mobile commerce environments. In Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings (pp. 616-626). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7345 LNAI). https://doi.org/10.1007/978-3-642-31087-4_63