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On Mining 2 Step Walking Pattern from Mobile Users

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Book cover Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3980))

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Abstract

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.

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References

  1. Agrawal, R., Srikat, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th VLDB, pp. 487–499 (1994)

    Google Scholar 

  2. Agrawal, R., Srikat, R.: Mining Sequential Patterns. In: Proc. of 11th ICDE, pp. 3–14 (1995)

    Google Scholar 

  3. Hofmann-Wellenhof, B., Lichtenegger, H., Collins, J.: Global Positioning System: Theory and Practice, 3rd revised edn. Springer, New York (1994)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  6. Forsyth, D.R.: Group Dynamics. Wadsworth, Belmont (1999)

    Google Scholar 

  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. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of ACM SIGMOD, pp. 1–12 (2000)

    Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. Zarchan, P.: Global Positioning System: Theory and Applications, vol. I. American Institute of Aeronautics and Astronautics (1996)

    Google Scholar 

  15. Reed Electronics Research RER – The mobile phone industry – a strategic overview (October 2002)

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  27. Goh, J., Taniar, D.: Mining Parallel Pattern from Mobile Users. International Journal of Business Data Communications and Networking 1(1), 50–76 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Lee, D.L., Zhu, M., Hu, H.: When location based services meet databases. Mobile Information Systems 1(2), 81–90 (2005)

    Google Scholar 

  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 

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Goh, J., Taniar, D. (2006). On Mining 2 Step Walking Pattern from Mobile Users. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751540_119

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  • DOI: https://doi.org/10.1007/11751540_119

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34070-6

  • Online ISBN: 978-3-540-34071-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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