An interval type 2 hesitant fuzzy MCDM approach and a fuzzy c means clustering for retailer clustering
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Owing to the advancements in information and telecommunication technologies, mobile location-based services are able to use previously collected mobile check-in data. This multi-dimensional data can provide new opportunities for research problems such as establishing new platforms for location-based advertising and location-based personalized recommendations. In other words, location data of potential customers indicate personal interests and visiting preferences. In some cases, customers’ preferences could not be easily determined or predicted while considering visiting patterns of mobile users. In this respect, this study provides a novel retailer segmentation approach based on multi-criteria decision-making (MCDM) combined fuzzy data clustering. The proposed model consists of two phases: (1) an interval type 2 hesitant MCDM approach for the determination of location perceived value and (2) retailer (store) clustering via different product sale prices with fuzzy data-based fuzzy c means (FcM) clustering. Proposed approach enables the simplification of FcM clustering adaptation to non-symmetric fuzzy data using dissimilarity measure. Using this integrated approach, advertisers and recommender system suppliers will be able to manage their product-special offerings to customers considering retailer segments and shopping mall characteristics. Additionally, the proposed approach constitutes the infrastructure of location-based recommender systems under imprecise environment.
KeywordsRetailer clustering Location clustering Fuzzy c means clustering Fuzzy data clustering Interval type 2 hesitant fuzzy sets
Compliance with ethical standards
Conflict of interest
All the authors declared that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
Informed consent was gathered from all individual participants included in the study.
- Agrawal V (2015) Novel fuzzy clustering algorithm for fuzzy data. In: 2015 Eighth international conference on contemporary computing (IC3), 20–22 Aug 2015Google Scholar
- Aliahmadipour L, Torra V, Eslami E (2017) On hesitant fuzzy clustering and clustering of hesitant fuzzy data. In: Fuzzy sets, rough sets, multisets and clustering, volume 671 of the series studies in computational intelligence, pp 157–168Google Scholar
- Cheverst K, Davies N, Mitchell K, Friday A, Efstratiou C (2000) Developing a context-aware electronic tourist guide: some issues and experiences. In: CHI ’00 Proceedings of the SIGCHI conference on human factors in computing systems, The Hague, The Netherlands, pp 17–24, 01–06 April 2000Google Scholar
- Han J, Kamber M (2001) Data mining concepts and techniques. Morgan Kauffman Publishers, Burlington, pp 5–33Google Scholar
- Lee LW, Chen SM (2013) Fuzzy decision making based on hesitant fuzzy linguistic term sets. In: Proceedings of the 5th Asian conference on intelligent information and database systems. Springer, Berlin, pp 21–30Google Scholar
- Li K, Du TC (2012) Building a targeted mobile advertising system for location-based services. Decis Support Syst 54(1):1–8Google Scholar
- Li J, Li L (2014) A location recommender based on a hidden Markov model: mobile social networks. J Organ Comput Electron Commer 24(2–3):257–270Google Scholar
- Mobile Marketing Association (2011) Mobile location based services marketing whitepaper. Technical Report. Mobile Marketing AssociationGoogle Scholar
- Oztaysi B, Gokdere U, Simsek EN, Oner SC (2016) A novel approach to segmentation using customer locations data and intelligent techniques. In: Kumar A, Dash MK, Trivedi SK (eds) Handbook of research on intelligent techniques and modeling applications in marketing analytics, IGI Global, Hershey, PA, USA, pp 21–39Google Scholar
- Ramya AR, Prasad Babu BR (2014) A novel concept of MANET architecture for location based service using circular data aggregation technique. Int J Innov Res Dev 3(1):252–8Google Scholar
- Sun Y, Fan H, Bakillah M, Zipf A (2013) Road-based travel recommendation using geo-tagged images. Comput Environ Urban Syst. https://doi.org/10.1016/j.compenvurbsys.2013.07.006 Google Scholar