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Issue No.11 - November (2009 vol.21)

pp: 1617-1628

Cheng-Tzu Wang , National Taipei University of Education, Taiwan

Shao-Ping Chen , Tamkang University, Taiwan

Ding-An Chiang , Tamkang University, Taiwan

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.36

ABSTRACT

Sequential pattern mining has been used to predict various aspects of customer buying behavior for a long time. Discovered sequence reveals the chronological relation between items and provides valuable information to aid in developing marketing strategies. Nevertheless, we can hardly know whether the buying is cyclic and how long the interval between the two consecutive items in the sequential pattern is. To solve this problem, in this paper, data mining skills and the fundamentals of statistics are combined to develop a set of algorithms to unearth the cyclic properties of discovered sequential patterns. The algorithms, coupled with the sequential pattern mining process, constitute a thorough scheme to analyze and predict likely consumer behavior. The proposed algorithms are implemented and applied to test against real data collected from a consumer goods company. The experimental results illustrate how the model can be used to predict likely purchases within a certain time frame. Consequently, marketing professionals can execute campaigns to favorably impact customers' behaviors.

INDEX TERMS

Association rules, data mining, frequency, sequential pattern, polynomial regression.

CITATION

Cheng-Tzu Wang, Shao-Ping Chen, Ding-An Chiang, "The Cyclic Model Analysis on Sequential Patterns",

*IEEE Transactions on Knowledge & Data Engineering*, vol.21, no. 11, pp. 1617-1628, November 2009, doi:10.1109/TKDE.2009.36REFERENCES