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 to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Veijalainene, J.: Transaction in Mobile Electronic Commerce, Dagstuhl Castle, Germany (September 1999)
Varshney, U., Vetter, R.J., Kalakota, R.: Mobile Commerce: A New Frontier. IEEE Computer 33 (October 2000)
Ben-Dor, A., Yakhini, Z.: Clustering gene expression Patterns. Journal of Computational Biology 6, 281–297 (1999)
Akyildiz, I.F., Wang, W.: The Predictive User Mobility Profile Framework for Wireless Multimedia Networks. IEEE/ACM (December 2004)
Soh, W.-S., Kim, H.: QoS Provisioning in Cellular Networks Based on Mobility Prediction Techniques. IEEE Comm. (January 2003)
Akyildiz, I.F., Mcnair, J., Ho, J.S.M., Uzunalioglu, H., Wang, W.: Mobility Management in Next-Generation Wireless System. IEEE, Los Alamitos (1999)
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)
Yun, C.-H., Chen, M.-S.: Mining Mobile Sequential Patterns In a Mobile Commerce Environment. IEEE Man, and Cybernetics (2007)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Int’l Conf. Data Eng. (ICDE 1995), pp. 3–14 (March 1995)
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)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery (1997)
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)
Zaki, M.J.: Efficient Enumeration of Frequent Sequences. In: Seventh Int’l Conf. Information and Knowledge Management, CIKM 1998 (1998)
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)
Lu, H., Han, J., Feng, L.: Stock Movement and n-Dimensional Inter-Transaction Association Rules. In: DMKD (June 1998)
Bettini, C., Wang, X.S., Jajodia, S.: Mining Temporal Relationships with Multiple Granularities in Time Sequences (1998)
Ózden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: ICDE 1998 (Feburary 1998)
Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the Discovery of Interesting Patterns in Association Rules. In: VLDB 1998 (August 1998)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: EDBT (1996)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: ICDE 1995 (March 1995)
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology (1999)
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)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rule between Sets of Items in Large Databases. ACM SIGMOD (May 1993)
Agrawal, R., Srikant, R.: Fast algorithm for mining Association rules in large databases (September 1994)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14 (March 1995)
Cannataro, M., Talia, D.: The Knowledge Grid. Communications of the ACM (2003)
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)
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)
Talia, D., Trunfio, P.: Mobile Data Mining on Small Devices through Web Services. John Wiley & Sons, Chichester (2007)
Berman, F.: From TeraGrid to Knowledge Grid. Communications of the ACM 44(11), 27–28 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)