The RFM (Recency, Frequency, Monetary) market analysis technique is a widely used in the marketing field to analyze customer behavior. The interest in machine learning has recently increased to utilize the increase in accumulated data. Therefore, an attempt was made to analyze data by combining the RFM technique and various algorithms. In this study, we attempted to classify customers through the RFM technique and k-means algorithm, which is a typical clustering algorithm. In a conventional experiment, there are many cases where the k value is designated as 8 or 9. However, in this experiment, the optimal k value for the data set was obtained using an internal evaluation method.

This work was supported by Institute for Information and communications Technology Promotion, funded by the government (Ministry of Science, ICT and Future Planning) under the contract. (2017-0-00862. Big data analysis based on IOT-based customer data collection and automatic recognition cloud customer sensing service system construction).

Authors and Affiliations

1. Department of Computer Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul, 143-747, South Korea

   Hyunjung Ji, Gyeongil Shin, Dongil Shin & Dongkyoo Shin

Corresponding author

Correspondence toDongil Shin.

Editors and Affiliations

1. Department of Computer Science and Engineering, Seoul University of Science and Technology, Seoul, Korea (Republic of)

   James J. Park

2. Department of Business Science, University of Salerno, Salerno, Italy

   Vincenzo Loia

3. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

   Gangman Yi

4. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

   Yunsick Sung

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