On Mining 2 Step Walking Pattern from Mobile Users

  • John Goh
  • David Taniar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)


Knowledge extraction from mobile user data analyzes data collected from mobile users, such as their user movement data in order to derive useful knowledge. User movement data is stored in a database which records the (x, y) coordinates that users have visited at any given point of time, for each mobile users. In this paper, we present a novel method for mining 2 step walking pattern from mobile users. The result of 2 step walking pattern provides the knowledge of how mobile users walks from one location of interest (LOI) to another in any given 2 steps. Case study for Walking-Matrix and Walking-Graph are provided along with performance evaluation.


Mobile User Pattern Mining Mobile Environment Knowledge Extraction Move Object Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikat, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Agrawal, R., Srikat, R.: Mining Sequential Patterns. In: Proc. of 11th ICDE, pp. 3–14 (1995)Google Scholar
  3. 3.
    Hofmann-Wellenhof, B., Lichtenegger, H., Collins, J.: Global Positioning System: Theory and Practice, 3rd revised edn. Springer, New York (1994)Google Scholar
  4. 4.
    Chakrabarti, S., Sarawagi, S., Dom, B.: Mining Surprising Patterns using Temporal Description Length. In: Proc. of 24th VLDB, pp. 606–617 (1998)Google Scholar
  5. 5.
    Forlizzi, L., Guting, R.H., Nardelli, E., Schneider, M.: A Data Model and Data Structures for Moving Objects Databases. ACM SIGMOD Record 260, 319–330 (2000)CrossRefGoogle Scholar
  6. 6.
    Forsyth, D.R.: Group Dynamics. Wadsworth, Belmont (1999)Google Scholar
  7. 7.
    Han, J., Dong, G., Yin, Y.: Efficient Mining of Partial Periodic Patterns in Time Series Database. In: Proc. of 15th ICDE, pp. 106–115 (1999)Google Scholar
  8. 8.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of ACM SIGMOD, pp. 1–12 (2000)Google Scholar
  9. 9.
    Han, J., Plank, A.W.: Background for Association Rules and Cost Estimate of Selected Mining Algorithms. In: Proc. of the 5th CIKM, pp. 73–80 (1996)Google Scholar
  10. 10.
    Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographical Information Databases. In: Proc of 4th Int Symp. on Advances in Spatial Databases, vol. 951, pp. 47–66 (1995)Google Scholar
  11. 11.
    Roddick, J.F., Lees, B.G.: Paradigms for Spatial and Spatio-Temporal Data Mining. In: Miller, H., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery, Taylor and Francis. Research Monographs in Geographical Information Systems, pp. 1–14 (2001)Google Scholar
  12. 12.
    Roddick, J.F., Spiliopoulou, M.: A Survey of Temporal Knowledge Discovery Paradigms and Methods. IEEE Trans. on Knowledge and Data Engineering 14(4), 750–767 (2002)CrossRefGoogle Scholar
  13. 13.
    Wang, W., Yang, J., Yu, P.S.: InfoMiner+: Mining Partial Periodic Patterns in Time Series Data. In: 2nd IEEE International Conference on Data Mining ICDM 2002, p. 725 (2002)Google Scholar
  14. 14.
    Zarchan, P.: Global Positioning System: Theory and Applications, vol. I. American Institute of Aeronautics and Astronautics (1996)Google Scholar
  15. 15.
    Reed Electronics Research RER – The mobile phone industry – a strategic overview (October 2002)Google Scholar
  16. 16.
    Varshney, U., Vetter, R., Kalakota, R.: Mobile commerce: A new frontier. IEEE Computer: Special Issue on E-commerce, 32–38 (October 2000)Google Scholar
  17. 17.
    Wang, Y., Lim, E.-P., Hwang, S.-Y.: On Mining Group Patterns from Mobile Users. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 287–296. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Wang, Y., Lim, E.-P., Hwang, S.-Y.: Efficient Group Pattern Mining Using Data Summarization. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 895–907. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Hwang, S.-Y., Liu, Y.-H., Chiu, J.-K., Lim, E.-P.: Mining Mobile Group Patterns: A Trajectory-Based Approach. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 713–718. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Cho, M., Pei, J., Wang, H., Wang, W.: Preference-based frequent pattern mining. International Journal of Data Warehousing and Mining 1(4), 56–77 (2005)CrossRefGoogle Scholar
  21. 21.
    Song, M.-B., Kang, S.-W., Park, K.-J.: On the design of energy-efficient location tracking mechanism in location-aware computing. Mobile Information Systems: An International Journal 1(2), 109–127 (2005)Google Scholar
  22. 22.
    Chen, S.Y., Loi, X.: Data mining from 1994 to 2004: an application-oriented review. International Journal of Business Intelligence and Data Mining 1(1), 4–21 (2005)CrossRefGoogle Scholar
  23. 23.
    Goh, J., Taniar, D.: Mobile user data static object mining (MUDSOM). The IEEE 20th International Conference on Advanced Information Networking and Applications (Submitted)Google Scholar
  24. 24.
    Goh, J., Taniar, D.: Static Group Pattern Mining (SGPM). In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 415–424. Springer, Heidelberg (2006) (Submitted)CrossRefGoogle Scholar
  25. 25.
    Goh, J., Taniar, D.: Mining Frequency Pattern from Mobile Users. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3215, pp. 795–801. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  26. 26.
    Goh, J., Taniar, D.: Mobile User Data Mining by Location Dependncies. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 225–231. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  27. 27.
    Goh, J., Taniar, D.: Mining Parallel Pattern from Mobile Users. International Journal of Business Data Communications and Networking 1(1), 50–76 (2005)CrossRefGoogle Scholar
  28. 28.
    Xiao, Y., Yao, J.F., Yang, G.: Discovering Frequent Embedded Subtree Patterns from Large Databases of Unordered Labeled Trees. International Journal of Data Warehousing and Mining 1(2), 70–92 (2005)CrossRefGoogle Scholar
  29. 29.
    Tjioe, H.C., Taniar, D.: Mining Association Rules in Data Warehouses. International Journal of Data Warehousing and Mining 1(3) (2005)Google Scholar
  30. 30.
    Häkkilä, J., Mäntyjärvi, J.: Combining Location-Aware Mobile Phone Applications and Multimedia Messaging. Journal of Mobile Multimedia 1(1), 18–32 (2005)Google Scholar
  31. 31.
    Tse, P.K.C., Lam, W.K., Ng, K.W., Chan, C.: An Implementation of Location-Aware Multimedia Information Download to Mobile System. Journal of Mobile Multimedia 1(1), 33–46 (2005)Google Scholar
  32. 32.
    Lee, D.L., Zhu, M., Hu, H.: When location based services meet databases. Mobile Information Systems 1(2), 81–90 (2005)Google Scholar
  33. 33.
    Jayaputera, J., Taniar, D.: Data retrieval for location-dependent queries in a multi-cell wireless environment. Mobile Information Systems 1(2), 91–108 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John Goh
    • 1
  • David Taniar
    • 1
  1. 1.School of Business SystemsMonash UniversityClaytonAustralia

Personalised recommendations