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Journal Article Subspace Projection-Based Clustering and Temporal ACRs Mining on MapReduce for Direct Marketing Service
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Authors
Heon Gyu Lee, Yong Hoon Choi, Hoon Jung, Yong Ho Shin
Issue Date
2015-04
Citation
ETRI Journal, v.37, no.2, pp.317-327
ISSN
1225-6463
Publisher
한국전자통신연구원 (ETRI)
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.4218/etrij.15.2314.0068
Project Code
14MC1100, Development of Implementation Technology for SMART Post, Jung Hoon
Abstract
A reliable analysis of consumer preference from a large amount of purchase data acquired in real time and an accurate customer characterization technique are essential for successful direct marketing campaigns. In this study, an optimal segmentation of post office customers in Korea is performed using a subspace projection-based clustering method to generate an accurate customer characterization from a high-dimensional census dataset. Moreover, a traditional temporal mining method is extended to an algorithm using the MapReduce framework for a consumer preference analysis. The experimental results show that it is possible to use parallel mining through a MapReduce-based algorithm and that the execution time of the algorithm is faster than that of a traditional method.
KSP Keywords
Clustering method, Consumer preferences, High-dimensional, MapReduce framework, Marketing campaigns, Marketing service, Mining method, Optimal segmentation, Parallel mining, Preference analysis, Real-Time