An Efficient Framework for Exploring Personal Pattern Mining and Prediction in Mobile Commerce
Nirmala. M1, Palanisamy. V2

1Prof. Nirmala M, Research Scholar, Department of CSE, Anna University, Coimbatore (Tamil Nadu), India.
2Dr. Palanisamy V, Principal, INFO Institute of Engineering, Coimbatore (Tamil Nadu), India.
Manuscript received on 11 January 2014 | Revised Manuscript received on 20 January 2014 | Manuscript Published on 30 January 2014 | PP: 18-22 | Volume-3 Issue-8, January 2014 | Retrieval Number: H1429013814/14©BEIESP
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Abstract: With the rapid advance of wireless communication technology and the increasing popularity of powerful portable devices, mobile users not only can access worldwide information from anywhere at any time but also use their mobile devices to make business transactions easily, e.g., via digital wallet. Meanwhile, the availability of location acquisition technology, e.g., Global Positioning System (GPS), facilitates easy acquisition of a moving trajectory, which records a user movement history. We propose a novel framework namely, Mobile Commerce Prediction (MCP) framework consists of three major components: 1) Similarity Model (SM) for measuring the similarities among stores and items, which are two basic mobile commerce entities 2) Mobile Commerce Pattern Mine (MCPM) algorithm for efficient discovery of mobile users’ Personal Mobile Commerce Patterns 3) Mobile Commerce User Behavior Predictor (MCUBP) for prediction of possible mobile user behaviors. We perform an extensive experimental evaluation by simulation and show that our proposals to produce excellent results.
Keywords: Association Rule Mining, Data Mining, Mobile Commerce, Pattern Mining and Prediction.

Scope of the Article: Regression and Prediction