A Novel Model for Finding Critical Products with Transaction Logs

  • Ping Yu Hsu
  • Chen Wan HuangEmail author
  • Shih Hsiang Huang
  • Pei Chi Chen
  • Ming Shien Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)


For the consumer market, finding valuable customers is the first priority and is assumed to assist companies in obtaining more profit. If we could discover critical products that are related with valuable customers, then it will lead to better marketing strategy to fulfill those essential customers. It will also assist companies in business development. This study selects real retail transaction data via the recency, frequency, and monetary (RFM) analysis and adopts the K-means algorithm to obtain results. Moreover, the Apriori algorithm with minimum support and skewness criteria is used to filter and find critical products. In this research, we found a novel methodology through setting the minimum support and skewness criteria and utilized the Apriori algorithm to identify 31 single critical products and 60 critical combinations (two products). This study assist companies in finding critical products and important customers, which is expected to provide an appropriate customer marketing strategy.


RFM K-means Association rules Skewness Frequent itemsets 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ping Yu Hsu
    • 1
  • Chen Wan Huang
    • 1
    Email author
  • Shih Hsiang Huang
    • 1
  • Pei Chi Chen
    • 1
  • Ming Shien Cheng
    • 2
  1. 1.Department of Business AdministrationNational Central UniversityTaoyuan CityTaiwan (R.O.C.)
  2. 2.Department of Industrial Engineering and ManagementMing Chi University of TechnologyNew Taipei CityTaiwan (R.O.C.)

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