Discovering valuable user behavior patterns in mobile commerce environments

Bai En Shie*, Hui Fang Hsiao, Philip S. Yu, S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential purchasing patterns with the moving paths, which are called mobile sequential patterns of the mobile users. Mobile sequential patterns can be applied not only for planning mobile commerce environments but also analyzing and managing online shopping websites. However, unit profits and purchased numbers of the items are not considered in traditional framework of mobile sequential pattern mining. Thus, the patterns with high utility (i.e., profit here) cannot be found. In view of this, we aim at integrating mobile data mining with utility mining for finding high utility mobile sequential patterns in this study. A novel algorithm called UMSP L (high Utility Mobile Sequential Pattern mining by a Level-wised method) is proposed to efficiently find high utility mobile sequential patterns. The experimental results show that the proposed algorithm has excellent performance under various system conditions.

Original languageEnglish
Title of host publicationNew Frontiers in Applied Data Mining - PAKDD 2011 International Workshops, Revised Selected Papers
Number of pages12
StatePublished - 7 Mar 2012
Event15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen, China
Duration: 24 May 201127 May 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7104 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011


  • high utility mobile sequential pattern
  • mobile environments
  • mobility pattern mining
  • Utility mining

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