Exploring frequent itemsets in sweltering climates

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

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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.

原文???core.languages.en_GB???
主出版物標題Data Mining and Big Data - 4th International Conference, DMBD 2019, Proceedings
編輯Ying Tan, Yuhui Shi
發行者Springer Verlag
頁面240-247
頁數8
ISBN(列印)9789813295629
DOIs
出版狀態已出版 - 2019
事件4th International Conference on Data Mining and Big Data, DMBD 2019 - Chiang Mai, Thailand
持續時間: 26 7月 201930 7月 2019

出版系列

名字Communications in Computer and Information Science
1071
ISSN(列印)1865-0929
ISSN(電子)1865-0937

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???event.eventtypes.event.conference???4th International Conference on Data Mining and Big Data, DMBD 2019
國家/地區Thailand
城市Chiang Mai
期間26/07/1930/07/19

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