A novel model for finding critical products with transaction logs

Ping Yu Hsu, Chen Wan Huang, Shih Hsiang Huang, Pei Chi Chen, Ming Shien Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
EditorsYing Tan, Qirong Tang, Yuhui Shi
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319938172
StatePublished - 2018
Event9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China
Duration: 17 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10942 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Swarm Intelligence, ICSI 2018


  • Association rules
  • Frequent itemsets
  • K-means
  • RFM
  • Skewness


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