Skip to main content

A Novel J2ME Service for Mining Incremental Patterns in Mobile Computing

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 101))

Abstract

Data mining services play an important role in the telecommunications industry. Considering the importance of data mining services to provide intelligence locally on devices on mobile environments, we propose a data mining service that adopts the embedded data mining algorithm according to situation. In this paper, we propose a novel data mining algorithm named J2ME-based Mobile Progressive Pattern Mine (J2MPP-Mine) for effective mobile computing. In J2MPP-Mine, we first propose a subset finder strategy named Subset-Finder (S-Finder) to find the possible subsets for prune. Then, we propose a Subset pruner algorithm (SB-Pruner) for determining the frequent pattern. Furthermore, we proposed the novel prediction strategy to determine the superset and remove the subset which generates a less number of sets due to different filtering pruning strategy. Finally, through the simulation our proposed methods were shown to deliver excellent performance in terms of efficiency, accuracy and applicability under various system conditions.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Veijalainene, J.: Transaction in Mobile Electronic Commerce, Dagstuhl Castle, Germany (September 1999)

    Google Scholar 

  2. Varshney, U., Vetter, R.J., Kalakota, R.: Mobile Commerce: A New Frontier. IEEE Computer 33 (October 2000)

    Google Scholar 

  3. Ben-Dor, A., Yakhini, Z.: Clustering gene expression Patterns. Journal of Computational Biology 6, 281–297 (1999)

    Article  Google Scholar 

  4. Akyildiz, I.F., Wang, W.: The Predictive User Mobility Profile Framework for Wireless Multimedia Networks. IEEE/ACM (December 2004)

    Google Scholar 

  5. Soh, W.-S., Kim, H.: QoS Provisioning in Cellular Networks Based on Mobility Prediction Techniques. IEEE Comm. (January 2003)

    Google Scholar 

  6. Akyildiz, I.F., Mcnair, J., Ho, J.S.M., Uzunalioglu, H., Wang, W.: Mobility Management in Next-Generation Wireless System. IEEE, Los Alamitos (1999)

    Google Scholar 

  7. Peng, W.-C., Chen, M.-S.: Mining User Moving Patterns for Personal Data Allocation in a Mobile Computing System. In: 29th Int’l Conf. Parallel Processing, pp. 573–580 (August 2000)

    Google Scholar 

  8. Yun, C.-H., Chen, M.-S.: Mining Mobile Sequential Patterns In a Mobile Commerce Environment. IEEE Man, and Cybernetics (2007)

    Google Scholar 

  9. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Int’l Conf. Data Eng. (ICDE 1995), pp. 3–14 (March 1995)

    Google Scholar 

  10. Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  11. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery (1997)

    Google Scholar 

  12. Wang, J., Chirn, G., Marr, T., Shapiro, B., Shasha, D., Zhang, K.: Combinatiorial Pattern Discovery for Scientific Data: Some Preliminary Results. In: Proc. 1994 ACM-SIGMOD Int’l Conf. Management of Data, SIGMOD 1994 (May 1994)

    Google Scholar 

  13. Zaki, M.J.: Efficient Enumeration of Frequent Sequences. In: Seventh Int’l Conf. Information and Knowledge Management, CIKM 1998 (1998)

    Google Scholar 

  14. Masseglia, F., Cathala, F., Poncelet, P.: The PSP Approach For Mining Sequential Patterns. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  15. Lu, H., Han, J., Feng, L.: Stock Movement and n-Dimensional Inter-Transaction Association Rules. In: DMKD (June 1998)

    Google Scholar 

  16. Bettini, C., Wang, X.S., Jajodia, S.: Mining Temporal Relationships with Multiple Granularities in Time Sequences (1998)

    Google Scholar 

  17. Ózden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: ICDE 1998 (Feburary 1998)

    Google Scholar 

  18. Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the Discovery of Interesting Patterns in Association Rules. In: VLDB 1998 (August 1998)

    Google Scholar 

  19. Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: EDBT (1996)

    Google Scholar 

  20. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: ICDE 1995 (March 1995)

    Google Scholar 

  21. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology (1999)

    Google Scholar 

  22. Pei, J., Han, J., Mortazavi-Asl, B., Zhu, H.: Mining Access Patterns Efficiently from Web Logs. In: Proceedings of 4th Pacific Asia Conference on Knowledge Discovery and Data Mining, Kyoto, Japan, pp. 396–407 (April 2000)

    Google Scholar 

  23. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rule between Sets of Items in Large Databases. ACM SIGMOD (May 1993)

    Google Scholar 

  24. Agrawal, R., Srikant, R.: Fast algorithm for mining Association rules in large databases (September 1994)

    Google Scholar 

  25. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14 (March 1995)

    Google Scholar 

  26. Cannataro, M., Talia, D.: The Knowledge Grid. Communications of the ACM (2003)

    Google Scholar 

  27. Foster, I.: Globus Toolkit Version 4: Software for Service-Oriented Systems. In: Jin, H., Reed, D., Jiang, W. (eds.) NPC 2005. LNCS, vol. 3779, pp. 2–13. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  28. Talia, D., Trunfio, P., Verta, O.: WSRF Services for Composing Distributed Data Mining Applications on Grids: Functionality and Performance. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 1080–1089. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  29. Talia, D., Trunfio, P.: Mobile Data Mining on Small Devices through Web Services. John Wiley & Sons, Chichester (2007)

    Google Scholar 

  30. Berman, F.: From TeraGrid to Knowledge Grid. Communications of the ACM 44(11), 27–28 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dubey, A.K., Shandilya, S.K. (2010). A Novel J2ME Service for Mining Incremental Patterns in Mobile Computing. In: Das, V.V., Vijaykumar, R. (eds) Information and Communication Technologies. ICT 2010. Communications in Computer and Information Science, vol 101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15766-0_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15766-0_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15765-3

  • Online ISBN: 978-3-642-15766-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics