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Mining Mobile Sequential Patterns in a Mobile Commerce Environment (2007)
| Citations: | 24 - 0 self |
BibTeX
@MISC{Yun07miningmobile,
author = {Ching-Huang Yun and et al.},
title = {Mining Mobile Sequential Patterns in a Mobile Commerce Environment},
year = {2007}
}
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Abstract
In this paper, we explore a new data mining capability for a mobile commerce environment. To better reflect the customer usage patterns in the mobile commerce environment, we propose an innovative mining model, called mining mobile sequential patterns, which takes both the moving patterns and purchase patterns of customers into consideration. How to strike a compromise among the use of various knowledge to solve the mining on mobile sequential patterns is a challenging issue. We devise three algorithms (algorithm TJLS, algorithm TJPT, and algorithm TJPF) for determining the frequent sequential patterns, which are termed large sequential patterns in this paper, from the mobile transaction sequences. Algorithm TJLS is devised in light of the concept of association rules and is used as the basic scheme. Algorithm TJPT is devised by taking both the concepts of association rules and path traversal patterns into consideration and gains performance improvement by path trimming. Algorithm TJPF is devised by utilizing the pattern family technique which is developed to exploit the relationship between moving and purchase behaviors, and thus is able to generate the large sequential patterns very efficiently. A simulation model for the mobile commerce environment is developed, and a synthetic workload is generated for performance studies. In mining mobile sequential patterns, it is shown by our experimental results that algorithm TJPF significantly outperforms others in both execution efficiency and memory saving, indicating the usefulness of the pattern family technique devised in this paper. It is shown by our results that by taking both moving and purchase patterns into consideration, one can have a better model for a mobile commerce system and is thus able to exploit the intrinsic relationship between these two important factors for the efficient mining of mobile sequential patterns.
Keyphrases
mobile sequential pattern mobile commerce environment algorithm tjls large sequential pattern algorithm tjpt purchase pattern pattern family technique algorithm tjpf association rule new data mining capability innovative mining model execution efficiency mobile computing basic scheme path trimming performance study challenging issue purchase behavior customer usage pattern mobile transaction sequence mining mobile sequential pattern various knowledge path traversal moving pattern user behavior gain performance improvement synthetic workload index term data mining mobile commerce system simulation model efficient mining memory saving frequent sequential pattern intrinsic relationship experimental result important factor
