DocumentCode :
260658
Title :
An efficient mobile commerce explorer for mobile user´s behavior pattern mining and prediction
Author :
Nagalakshmi, S. ; Sumathi, R.
Author_Institution :
Comput. Sci. & Eng., J.J. Coll. of Eng. & Technol., Tiruchirappalli, India
fYear :
2014
fDate :
27-28 Feb. 2014
Firstpage :
1
Lastpage :
7
Abstract :
Due to wide range of potential applications, research on mobile commerce has received a lot of interests from both of the industry and academia. Among them, one of the active topic areas is the mining and prediction of user´s mobile commerce behaviors such as their movements and purchase transactions. In this paper, we explore a new data mining capability for a mobile commerce environment based on location based service (LBS). A novel framework, called Mobile Commerce Explorer (MCE) used for mining and prediction of mobile users´ movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three main components like 1) Similarity Inference Model (SIM)measuring the similarities among stores and items; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm used for finding the mobile user´s Personal Mobile Commerce Patterns (PMCPs); 3) Mobile Commerce Behavior Predictor (MCBP) for prediction the future mobile user behaviors. Location-based service (LBS) is used for recommending the stores and items previously unknown to a user. The Administrator controller will monitor the whole transaction process, so there will be a secure transaction.
Keywords :
consumer behaviour; data mining; mobile commerce; purchasing; transaction processing; LBS; MCBP; MCE; PMCP-Mine algorithm; SIM; data mining capability; location based service; mobile commerce behavior predictor; mobile commerce explorer; mobile user behavior pattern mining; mobile user purchase transactions; personal mobile commerce pattern mine algorithm; similarity inference model; Algorithm design and analysis; Business; Data mining; Mobile communication; Prediction algorithms; Predictive models; Vectors; Datamining; mobile commerce;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Communication and Embedded Systems (ICICES), 2014 International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4799-3835-3
Type :
conf
DOI :
10.1109/ICICES.2014.7033754
Filename :
7033754
Link To Document :
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