The Cyclic Model Analysis on Sequential Patterns

Authors:
Ding-An Chiang;Cheng-Tzu Wang;Shao-Ping Chen;Chun-Chi Chen
Affiliations:
Tamkang University, Taiwan;National Taipei University of Education, Taiwan;Tamkang University, Taiwan;Tamkang University, Taiwan
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2009

Citing 0
Cited 4

Analysis on repeat-buying patterns

Knowledge-Based Systems
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases

Journal of Systems and Software
Mining high coherent association rules with consideration of support measure

Expert Systems with Applications: An International Journal
Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

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.