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All issues Volume 465 (2023) E3S Web of Conf., 465 (2023) 02005 References
Open Access
Issue
E3S Web of Conf.
Volume 465, 2023
8th International Conference on Industrial, Mechanical, Electrical and Chemical Engineering (ICIMECE 2023)
Article Number 02005
Number of page(s) 7
Section Symposium on Electrical, Information Technology, and Industrial Engineering
DOI https://doi.org/10.1051/e3sconf/202346502005
Published online 18 December 2023
  1. H. Li, Y. J. Wu, S. Zhang, and J. Zou, “Temporary rules of retail product sales time series based on the matrix profile,” J. Retail. Consum. Serv., vol. 60, p. 102431, May 2021, doi: 10.1016/j.jretconser.2020.102431. [CrossRef] [Google Scholar]
  2. S. Gupta and D. Ramachandran, “Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach,” J. Retail., vol. 97, no. 4, pp. 597–620, Dec. 2021, doi: 10.1016/j.jretai.2021.01.008. [CrossRef] [Google Scholar]
  3. G. J. Krishna and V. Ravi, “High utility itemset mining using binary differential evolution: An application to customer segmentation,” Expert Syst. Appl., vol. 181, p. 115122, Nov. 2021, doi: 10.1016/j.eswa.2021.115122. [CrossRef] [Google Scholar]
  4. H. Choi, E.-K. Choi, B. Yoon, and H.-W. Joung, “Understanding food truck customers: Selection attributes and customer segmentation,” Int. J. Hosp. Manag., vol. 90, p. 102647, Sep. 2020, doi: 10.1016/j.ijhm.2020.102647. [CrossRef] [Google Scholar]
  5. A. J. Christy, A. Umamakeswari, L. Priyatharsini, and A. Neyaa, “RFM ranking – An effective approach to customer segmentation,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 10, pp. 1251– 1257, Dec. 2021, doi: 10.1016/j.jksuci.2018.09.004. [Google Scholar]
  6. D. Turner, A. Lucieer, Z. Malenovský, D. King, and S. A. Robinson, “Assessment of Antarctic moss health from multi-sensor UAS imagery with Random Forest Modelling,” Int. J. Appl. Earth Obs. Geoinf., vol. 68, pp. 168–179, Jun. 2018, doi: 10.1016/j.jag.2018.01.004. [Google Scholar]
  7. Y.-L. Chen, M.-H. Kuo, S.-Y. Wu, and K. Tang, “Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data,” Electron. Commer. Res. Appl., vol. 8, no. 5, pp. 241–251, Oct. 2009, doi: 10.1016/j.elerap.2009.03.002. [CrossRef] [Google Scholar]
  8. P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” J. King Saud Univ. - Comput. Inf. Sci., Dec. 2019, doi: 10.1016/j.jksuci.2019.12.011. [Google Scholar]
  9. M. Hosseini, S. Shajari, and M. Akbarabadi, “Identifying multi-channel value co-creator groups in the banking industry,” J. Retail. Consum. Serv., p. 102312, Sep. 2020, doi: 10.1016/j.jretconser.2020.102312. [Google Scholar]
  10. M. Frasquet, M. Ieva, and C. Ziliani, “Online channel adoption in supermarket retailing,” J. Retail. Consum. Serv., vol. 59, p. 102374, Mar. 2021, doi: 10.1016/j.jretconser.2020.102374. [CrossRef] [Google Scholar]
  11. S.-C. Wang, Y.-T. Tsai, and Y.-S. Ciou, “A hybrid big data analytical approach for analyzing customer patterns through an integrated supply chain network,” J. Ind. Inf. Integr., vol. 20, p. 100177, Dec. 2020, doi: 10.1016/j.jii.2020.100177. [Google Scholar]
  12. D. Kim, K. Park, D.-J. Lee, and Y. Ahn, “Predicting mobile trading system discontinuance: The role of attention,” Electron. Commer. Res. Appl., vol. 44, p. 101008, Nov. 2020, doi: 10.1016/j.elerap.2020.101008. [CrossRef] [Google Scholar]
  13. J. Zhou, L. Zhai, and A. A. Pantelous, “Market segmentation using high-dimensional sparse consumers data,” Expert Syst. Appl., vol. 145, p. 113136, May 2020, doi: 10.1016/j.eswa.2019.113136. [CrossRef] [Google Scholar]
  14. J. Maia et al., “Evolving clustering algorithm based on mixture of typicalities for stream data mining,” Futur. Gener. Comput. Syst., vol. 106, pp. 672–684, May 2020, doi: 10.1016/j.future.2020.01.017. [CrossRef] [Google Scholar]
  15. E. Patel and D. S. Kushwaha, “Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model,” Procedia Comput. Sci., vol. 171, pp. 158–167, 2020, doi: 10.1016/j.procs.2020.04.017. [CrossRef] [Google Scholar]
  16. J. J. López, J. A. Aguado, F. Martín, F. Muñoz, A. Rodríguez, and J. E. Ruiz, “Hopfield–K-Means clustering algorithm: A proposal for the segmentation of electricity customers,” Electr. Power Syst. Res., vol. 81, no. 2, pp. 716–724, Feb. 2011, doi: 10.1016/j.epsr.2010.10.036. [CrossRef] [Google Scholar]
  17. Y. Li, X. Chu, D. Tian, J. Feng, and W. Mu, “Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm,” Appl. Soft Comput., vol. 113, p. 107924, Dec. 2021, doi: 10.1016/j.asoc.2021.107924. [CrossRef] [Google Scholar]
  18. K. Deeparani and P. Sudhakar, “Efficient image segmentation and implementation of K-means clustering,” Mater. Today Proc., vol. 45, pp. 8076–8079, 2021, doi: 10.1016/j.matpr.2021.01.154. [CrossRef] [Google Scholar]
  19. A. Khumaidi, “Data Mining For Predicting The Amount Of Coffee Production Using CRISP-DM Method,” J. Techno Nusa Mandiri, vol. 17, no. 1, pp. 1–8, Feb. 2020, doi: 10.33480/techno.v17i1.1240. [Google Scholar]
  20. M. O. Adeniyi et al., “Dynamic model of COVID-19 disease with exploratory data analysis,” Sci. African, vol. 9, p. e00477, Sep. 2020, doi: 10.1016/j.sciaf.2020.e00477. [Google Scholar]
  21. B. Peromingo, D. Caballero, A. Rodríguez, A. Caro, and M. Rodríguez, “Application of data mining techniques to predict the production of aflatoxin B1 in dry-cured ham,” Food Control, vol. 108, p. 106884, Feb. 2020, doi: 10.1016/j.foodcont.2019.106884. [CrossRef] [Google Scholar]
  22. A. M. Noor, H. Yazid, Z. Zakaria, and A. M. Noo, “Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes,” Eng. Appl. Sci. Res., vol. 47, no. 2, pp. 129–136, 2020, doi: 10.14456/easr.2020.13. [Google Scholar]
  23. A. Žiberna, “k-means-based algorithm for blockmodeling linked networks,” Soc. Networks, vol. 61, pp. 153–169, May 2020, doi: 10.1016/j.socnet.2019.10.006. [CrossRef] [Google Scholar]
  24. B. Hajer, B. Arwa, H. Lobna, and G. Khaled, “Intention Mining Data preprocessing based on Multi-Agents System,” Procedia Comput. Sci., vol. 176, pp. 888–897, 2020, doi: 10.1016/j.procs.2020.09.084. [CrossRef] [Google Scholar]
  25. N. Huyghues-Beaufond, S. Tindemans, P. Falugi, M. Sun, and G. Strbac, “Robust and automatic data cleansing method for short-term load forecasting of distribution feeders,” Appl. Energy, vol. 261, p. 114405, Mar. 2020, doi: 10.1016/j.apenergy.2019.114405. [CrossRef] [Google Scholar]
  26. Q. Wan and Y. Yu, “Power load pattern recognition algorithm based on characteristic index dimension reduction and improved entropy weight method,” Energy Reports, vol. 6, pp. 797– 806, Dec. 2020, doi: 10.1016/j.egyr.2020.11.129. [CrossRef] [Google Scholar]

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