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The Cyclic Model Analysis on Sequential Patterns
November 2009 (vol. 21 no. 11)
pp. 1617-1628
ASCII Text
x 
 
Ding-An Chiang, Cheng-Tzu Wang, Shao-Ping Chen, Chun-Chi Chen, "The Cyclic Model Analysis on Sequential Patterns," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 11, pp. 1617-1628, November, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2009.36,
author = {Ding-An Chiang and Cheng-Tzu Wang and Shao-Ping Chen and Chun-Chi Chen},
title = {The Cyclic Model Analysis on Sequential Patterns},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {21},
number = {11},
issn = {1041-4347},
year = {2009},
pages = {1617-1628},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.36},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - The Cyclic Model Analysis on Sequential Patterns
IS - 11
SN - 1041-4347
SP1617
EP1628
EPD - 1617-1628
A1 - Ding-An Chiang,
A1 - Cheng-Tzu Wang,
A1 - Shao-Ping Chen,
A1 - Chun-Chi Chen,
PY - 2009
KW - Association rules
KW - data mining
KW - frequency
KW - sequential pattern
KW - polynomial regression.
VL - 21
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Ding-An Chiang, Tamkang University, Taiwan
Cheng-Tzu Wang, National Taipei University of Education, Taiwan
Shao-Ping Chen, Tamkang University, Taiwan
Chun-Chi Chen, Tamkang University, Taiwan
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.

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Index Terms:
Association rules, data mining, frequency, sequential pattern, polynomial regression.
Citation:
Ding-An Chiang, Cheng-Tzu Wang, Shao-Ping Chen, Chun-Chi Chen, "The Cyclic Model Analysis on Sequential Patterns," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 11, pp. 1617-1628, Nov. 2009, doi:10.1109/TKDE.2009.36
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