Exploring frequent itemsets in sweltering climates

Ping Yu Hsu, Chen Wan Huang, Ming Shien Cheng, Yen Huei Ko, Cheng Han Tsai, Ni Xu

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


With digital transformation and in the highly competitive retail market, it is important to understand customer needs and environmental changes. Moreover, obtain more profits through novel data mining technology is essential as well. Thus, the following questions should be addressed. Does climate influence the purchasing willingness of consumers? Do consumers buy different products based on the weather temperature? Few studies have used weather data and multilevel association rules to determine significant product combinations. In this study, real retail transaction records, temperature interval, and hierarchy class information were combined to develop a novel method and an improved association rule algorithm for exploring frequently purchased items under different weather temperatures. Twenty-six significant product combinations were discovered under particular temperatures. The results of this study can be used to enhance the purchasing willingness of consumers under a particular weather temperature and assist the retail industry to develop marketing strategies.

Original languageEnglish
Title of host publicationData Mining and Big Data - 4th International Conference, DMBD 2019, Proceedings
EditorsYing Tan, Yuhui Shi
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9789813295629
StatePublished - 2019
Event4th International Conference on Data Mining and Big Data, DMBD 2019 - Chiang Mai, Thailand
Duration: 26 Jul 201930 Jul 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference4th International Conference on Data Mining and Big Data, DMBD 2019
CityChiang Mai


  • Apriori algorithm
  • Frequent itemsets
  • Multilevel association rules


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