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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Subspace Projection-Based Clustering and Temporal ACRs Mining on MapReduce for Direct Marketing Service

  • Lee, Heon Gyu (IT Convergence Technology Research Laboratory, ETRI) ;
  • Choi, Yong Hoon (IT Convergence Technology Research Laboratory, ETRI) ;
  • Jung, Hoon (IT Convergence Technology Research Laboratory, ETRI) ;
  • Shin, Yong Ho (School of Business, Yeungnam University)
  • Received : 2014.08.04
  • Accepted : 2014.10.24
  • Published : 2015.04.01
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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.

Keywords

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