Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22, 207–216 (1993)
Bahng, Y., Kincade, D.H.: The relationship between temperature and sales: sales data analysis of a retailer of branded women’s business wear. Int. J. Retail Distrib. Manag. 40, 410–426 (2012)
Baralis, E., Cagliero, L., Cerquitelli, T., D’Elia, V., Garza, P.: Expressive generalized itemsets. Inf. Sci. 278, 327–343 (2014)
Bertrand, J.-L., Brusset, X., Fortin, M.: Assessing and hedging the cost of unseasonal weather: case of the apparel sector. Eur. J. Oper. Res. 244, 261–276 (2015)
Chen, M.-C., Lin, C.-P.: A data mining approach to product assortment and shelf space allocation. Expert Syst. Appl. 32, 976–986 (2007)
Murray, K.B., Di Muro, F., Finn, A., Popkowski Leszczyc, P.: The effect of weather on consumer spending. J. Retail. Consum. Serv. 17, 512–520 (2010)
Nourani, V., Sattari, M.T., Molajou, A.: Threshold-based hybrid data mining method for long-term maximum precipitation forecasting. Water Resour. Manag. 31, 2645–2658 (2017)
Persinger, M.A., Levesque, B.F.: Geophysical variables and behavior: XII: the weather matrix accommodates large portions of variance of measured daily mood. Percept. Mot. Skills 57(February), 868–870 (1983)
Reder, M., Yürüşen, N.Y., Melero, J.J.: Data-driven learning framework for associating weather conditions and wind turbine failures. Reliab. Eng. Syst. Saf. 169, 554–569 (2018)
Srikant, R., Agrawal, R.: Mining generalized association rules. Future Gener. Comput. Syst. 13, 161–180 (1997)
Zhao, J., Guo, Y., Xiao, X., Wang, J., Chi, D., Guo, Z.: Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method. Appl. Energy 197, 183–202 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hsu, P.Y., Huang, C.W., Cheng, M.S., Ko, Y.H., Tsai, CH., Xu, N. (2019). Exploring Frequent Itemsets in Sweltering Climates. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_25
Download citation
DOI: https://doi.org/10.1007/978-981-32-9563-6_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9562-9
Online ISBN: 978-981-32-9563-6
eBook Packages: Computer ScienceComputer Science (R0)