Abstract
Location Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely adapted to understand consumer location visiting tendency. In this study, we propose a strategy of analysis, by combining Adaboost algorithm and the fuzzy decision tree methodology for fuzzy data. The results emphasis on the heterogeneity in consumers’ place preferences and implications for marketing are offered.
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References
Anagnostopoulos, C., Hadjiefthymiades, S., Kolomvatsos, K.: Time-optimized user grouping in Location Based Services. Comput. Netw. 81, 220–244 (2015)
Pingley, A., Yu, W., Zhang, N., Fu, X., Zhao, W.: A context-aware scheme for privacy-preserving location-based services. Comput. Netw. 56, 2551–2568 (2012)
Wu, C.H., Kao, S.C., Wu, C.C., Huang, S.: Location-aware service applied to mobile short message advertising: design, development, and evaluation. Inf. Process. Manag. 51(5), 625–642 (2015)
Cheng, L.C., Wang, H.-A.: A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl. Soft Comput. 18, 290–301 (2014)
Mobile Marketing Association: Mobile location based services marketing whitepaper, Technical report, Mobile Marketing Association (2011)
Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a context-aware electronic tourist guide: some issues and experiences. In: CHI 2000 Proceedings of the SIGCHI conference on Human Factors in Computing Systems, pp. 17–24, The Hague, The Netherlands—01–06 April 2000 (2000)
Li, J., Li, L.: A location recommender based on a hidden markov model: mobile social networks. J. Organ. Comput. Electron. Commer. 24(2–3), 257–270 (2014)
Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333 (2014)
Bockstaller, C., Beauchet, S., Vincent, Manneville V., Amiaud, B., Botreau, R.: A tool to design fuzzy decision trees for sustainability assessment. Environ. Model Softw. 97, 130–144 (2017)
Lertworaprachaya, Y., Yang, Y., John, R.: Interval-valued fuzzy decision trees. In: International Conference on Fuzzy Systems (2010)
Farias, G., Dormido-Canto, S., Vega, J., MartĂnez, I., Alfaro, L., MartĂnez, F.: Adaboost classification of TJ-II Thomson Scattering images. Fusion Eng. Des. 123, 759–763 (2017)
D’Urso, P., Disegna, M., Massari, R., Prayag, G.: Bagged fuzzy clustering for fuzzy data: an application to a tourism market. Knowl.-Based Syst. 73, 335–346 (2015)
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Oner, S.C., Oztaysi, B. (2020). A Fuzzy Base Classifier for Fuzzy Data Included Location Segmentation. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_22
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DOI: https://doi.org/10.1007/978-3-030-23756-1_22
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