Open Access Open Access  Restricted Access Subscription or Fee Access

Architecture for Prediction of Mobile Transaction using Historical Mobile Location and Transaction Data

Savita Patil, V. Hariharan

Abstract


With the increase in the number of mobile commerce transaction, it is beneficial to have architecture to provide better mobile commerce experience and facilities to users. The locations and mobile commerce data generated by users can be analyzed using data mining techniques to arrive at similarity of items and stores. Similarity data clubbed with the patterns in historical transactions of user can be used for prediction of mobile transaction of the user. Here we propose a novel architecture called as Mobile Commerce Explorer. The MCE framework consists of three major components: 1)Similarity Inference Model(SIM)for measuring the similarities among stores and items, which are two basic mobile commerce entities considered in this paper; 2)Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users’ Personal Mobile Commerce Patterns(PMCPs); and 3)Mobile Commerce Behavior Predictor(MCBP) for prediction of possible mobile user behaviors.

Keywords


Data Mining, Mobile Commerce

Full Text:

PDF

References


R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rule between Sets of Items in Large Databases,” Proc. ACM SIGMOD Conf. Management of Data, pp. 207-216, May 1993.

R. Agrawal and R. Srikant, “Fast Algorithm for Mining Association Rules,” Proc. Int’l Conf. Very Large Databases, pp. 478-499, Sept. 1994.

R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. Int’l Conf. Data Eng., pp. 3-14, Mar.1995.

J. Han and Y. Fu, “Discovery of Multiple-Level Association Rules in Large Database,”Proc. Int’l Conf. Very Large Data Bases,pp. 420-431, Sept. 1995.

V.S. Tseng and W.C. Lin, “Mining Sequential Mobile Access Patterns Efficiently in Mobile Web Systems,”Proc. Int’l Conf. Advanced Information Networking and Applications, pp. 867-871, Mar. 2005

Y. Tao, C. Faloutsos, D. Papadias, and B. Liu, “Prediction and Indexing of Moving Objects with Unknown motion patterns,” Proc. ACM SIGMOD Conf. Management of Data, pp. 611-622, June 2004.

U. Varshney, R.J. Vetter, and R. Kalakota, “Mobile Commerce: A New Frontier,” Computer, vol. 33, no. 10, pp. 32-38, Oct. 2000

G. Jeh and J. Widom, “SimRank: A Measure of Structural-Context Similarity,”Proc. Int’l Conf. Knowledge Discovery and Data Mining, pp. 538-543, July 2002.

V.S. Tseng and K.W. Lin, “Efficient Mining and Prediction of User Behavior Patterns in Mobile Web Systems,” Information and Software Technology, vol. 48, no. 6, pp. 357-369, June 2006.

V.S. Tseng and C.F. Tsui, “Mining Multi-Level and Location-Aware A Associated Service Patterns in Mobile Environments,” IEEE Trans. Systems, Man and Cybernetics: Part B, vol. 34, no. 6, pp. 2480-2485, Dec. 2004.

U. Varshney, R.J. Vetter, and R. Kolkata, “Mobile Commerce: A New Frontier,” Computer, vol. 33, no. 10, pp. 32-38, Oct. 2000.

Y. Ye, Y. Zheng, Y. Chen, J. Feng, and X. Xie, “Mining Individual Life Pattern Based on Location History,” Proc. Int’l Conf. Mobile Data Management Systems, Services and Middleware, pp. 1-10, May 2009.

X. Yin, J. Han, and P.S. Yu, “LinkClus: Efficient Clustering via Heterogeneous Semantic Links,” Proc. Int’l Conf. Very Large Data Bases,pp. 427-438, Aug. 2006.

Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, “Mining Interesting Location and Travel Sequences from GPS Trajectories,” Proc. Int’l World Wide Web Conf.,pp. 791-800, Apr. 2009.

J.-S. Park, M.-S. Chan, and P.S. Yu, “An Effective Hash Based Algorithm for Mining Association Rules,” Proc. ACM SIGMOD Conf. Management of Data, pp. 175-186, May 1995.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.