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학술지 Subspace Projection-Based Clustering and Temporal ACRs Mining on MapReduce for Direct Marketing Service
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저자
이헌규, 최용훈, 정훈, 신용호
발행일
201504
출처
ETRI Journal, v.37 no.2, pp.317-327
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.15.2314.0068
협약과제
14MC1100, SMART Post 구축 기술 개발, 정훈
초록
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.
키워드
Customer characterization, Direct marketing, MapReduce framework, Subspace projection, Temporal associative classification, Temporal mining
KSP 제안 키워드
Clustering method, Consumer preferences, High-dimensional, MapReduce framework, Marketing campaigns, Marketing service, Mining method, Optimal segmentation, Parallel mining, Preference analysis, Real-Time