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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7345)


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.


Mobile sequential pattern mobility pattern mining utility mining mobile commerce environment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proc. of 11th Int’l Conf. on Data Mining, pp. 3–14 (1995)Google Scholar
  2. 2.
    Ahmed, C.F., Tanbeer, S.K., Jeong, B.-S., Lee, Y.-K.: Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases. IEEE Transaction on Knowledge and Data Engineering 21(12), 1708–1721 (2009)CrossRefGoogle Scholar
  3. 3.
    Li, Y.-C., Yeh, J.-S., Chang, C.-C.: Isolated items discarding strategy for discovering high utility itemsets. Data & Knowledge Engineering 64(1), 198–217 (2008)CrossRefGoogle Scholar
  4. 4.
    Liu, Y., Liao, W.-K., Choudhary, A.: A fast high utility itemsets mining algorithm. In: Proc. of Utility-Based Data Mining (2005)Google Scholar
  5. 5.
    Lu, E.H.-C., Tseng, V.S.: Mining cluster-based mobile sequential patterns in location-based service environments. In: Proc. of IEEE MDM, pp. 273–278 (2009)Google Scholar
  6. 6.
    Lu, E.H.-C., Tseng, V.S., Yu, P.S.: Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments. IEEE Transactions on Knowledge and Data Engineering 23(6), 914–927 (2011)CrossRefGoogle Scholar
  7. 7.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Transactions on Knowledge and Data Engineering 16(10) (2004)Google Scholar
  8. 8.
    Shie, B.-E., Hsiao, H.-F., Tseng, V.S., Yu, P.S.: Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part I. LNCS, vol. 6587, pp. 224–238. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Shie, B.-E., Hsiao, H.-F., Yu, P.S., Tseng, V.S.: Discovering Valuable User Behavior Patterns in Mobile Commerce Environments. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD Workshops 2011. LNCS, vol. 7104, pp. 77–88. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Tseng, V.S., Wu, C.-W., Shie, B.-E., Yu, P.S.: UP-Growth: An Efficient Algorithm for High Utility Itemsets Mining. In: Proc. of the 16th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD 2010), pp. 253–262 (2010)Google Scholar
  11. 11.
    Yun, C.-H., Chen, M.-S.: Mining Mobile Sequential Patterns in a Mobile Commerce Environment. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 37(2) (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bai-En Shie
    • 1
  • Ji-Hong Cheng
    • 1
  • Kun-Ta Chuang
    • 1
  • Vincent S. Tseng
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTaiwan, ROC

Personalised recommendations