Soft Computing

, Volume 22, Issue 15, pp 4971–4987 | Cite as

An interval type 2 hesitant fuzzy MCDM approach and a fuzzy c means clustering for retailer clustering

  • Sultan Ceren OnerEmail author
  • Başar Oztaysi


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.


Retailer 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.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was gathered from all individual participants included in the study.

Supplementary material

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  1. Agrawal V (2015) Novel fuzzy clustering algorithm for fuzzy data. In: 2015 Eighth international conference on contemporary computing (IC3), 20–22 Aug 2015Google Scholar
  2. 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
  3. Anagnostopoulos C, Hadjiefthymiades S, Kolomvatsos K (2015) Time-optimized user grouping in location based services. Comput Netw 81:220–244CrossRefzbMATHGoogle Scholar
  4. Chen N, Xu ZS, Xia MM (2014) Hierarchical hesitant fuzzy K-means clustering algorithm. Appl Math A J Chin Univ 29:1–17MathSciNetCrossRefzbMATHGoogle Scholar
  5. 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
  6. Coppi R, D’Urso P, Giordani P (2012) Fuzzy and possibilistic clustering for fuzzy data. Comput Stat Data Anal 56(4):915–927MathSciNetCrossRefzbMATHGoogle Scholar
  7. D’Urso P, Giordani P (2006) A weighted fuzzy c-means clustering model for fuzzy data. Comput Stat Data Anal 50(6):1496–1523MathSciNetCrossRefzbMATHGoogle Scholar
  8. D’Urso P, Disegna M, Massari R, Prayag G (2015) Bagged fuzzy clustering for fuzzy data: an application to a tourism market. Knowl Based Syst 73:335–346CrossRefGoogle Scholar
  9. Devi MU, Gandhi GM (2015) An enhanced fuzzy clustering and expectation maximization framework based matching semantically similar sentences. Proc Comput Sci 57:1149–1159CrossRefGoogle Scholar
  10. Erilli NA, Yolcu U, Eğrioğlu E, Aladağ ÇK, Öner Y (2011) Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. Expert Syst Appl 38(3):2248–2252CrossRefGoogle Scholar
  11. Fan S, Lau RYK, Zhao JL (2015) Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Res 2(1):28–32CrossRefGoogle Scholar
  12. Fu P, Yin H (2012) Logistics enterprise evaluation model based on fuzzy clustering analysis. Phys Proc 24(Part C):1583–1587CrossRefGoogle Scholar
  13. Fu Q, Wang Z, Jiang Q (2010) Delineating soil nutrient management zones based on fuzzy clustering optimized by PSO. Math Comput Modell 51(11–12):1299–1305CrossRefzbMATHGoogle Scholar
  14. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333CrossRefGoogle Scholar
  15. Gosain A, Dahiya S (2016) Performance analysis of various fuzzy clustering algorithms: a review. Proc Comput Sci 79:100–111CrossRefGoogle Scholar
  16. Han L, Chen G (2009) A fuzzy clustering method of construction of ontology-based user profiles. Adv Eng Softw 40(7):535–540CrossRefzbMATHGoogle Scholar
  17. Han J, Kamber M (2001) Data mining concepts and techniques. Morgan Kauffman Publishers, Burlington, pp 5–33Google Scholar
  18. Hipp J, Güntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explor Newsl 2(1):58–64CrossRefGoogle Scholar
  19. Hu J, Xiao K, Chen X, Liu Y (2015) Interval type-2 hesitant fuzzy set and its application in multi-criteria decision making. Comput Ind Eng 87:91–103CrossRefGoogle Scholar
  20. Junglas IA, Watson RT (2008) Location-based services. Commun ACM 51(3):65–69CrossRefGoogle Scholar
  21. Kuo MH, Chen LC, Liang CW (2009) Building and evaluating a location-based service recommendation system with a preference adjustment mechanism. Expert Syst Appl 36:3543–3554CrossRefGoogle Scholar
  22. 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
  23. Lee S, Kim KJ, Sundar SS (2015) Customization in location-based advertising: effects of tailoring source, locational congruity, and product involvement on ad attitudes. Comput Hum Behav 51:336–343CrossRefGoogle Scholar
  24. Li K, Du TC (2012) Building a targeted mobile advertising system for location-based services. Decis Support Syst 54(1):1–8Google Scholar
  25. 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
  26. Li YM, Chou CL, Lin LF (2014) A social recommender mechanism for location-based group commerce. Inf Sci 274:125–142CrossRefGoogle Scholar
  27. Lin TTC, Paragas F, Goh D, Bautista JR (2016) Developing location-based mobile advertising in Singapore: a socio-technical perspective. Technol Forecast Soc Change 103:334–349CrossRefGoogle Scholar
  28. Mendel JM, John RB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127CrossRefGoogle Scholar
  29. Mobile Marketing Association (2011) Mobile location based services marketing whitepaper. Technical Report. Mobile Marketing AssociationGoogle Scholar
  30. Oztaysi B, Isik M (2014) Supplier evaluation using fuzzy clustering. In: Kahraman C, Oztaysi B (eds) Supply chain management under fuzziness: recent developments and techniques. Springer, Berlin, pp 61–80CrossRefGoogle Scholar
  31. 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
  32. Park DH, Kim HK, Choi Y, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39:10059–10072CrossRefGoogle Scholar
  33. Pingley A, Yu W, Zhang N, Fu X, Zhao W (2012) A context-aware scheme for privacy-preserving location-based services. Comput Netw 56:2551–2568CrossRefGoogle Scholar
  34. 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
  35. Ren M, Wang B, Liang Q, Fu G (2010) Classified real-time flood forecasting by coupling fuzzy clustering and neural network. Int J Sediment Res 25(2):134–148CrossRefGoogle Scholar
  36. Rodriguez RM, Martinez L, Herrera F (2012) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119CrossRefGoogle Scholar
  37. Rodriguez RM, Martinez L, Herrera F (2013) A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Inf Sci 241:28–42MathSciNetCrossRefzbMATHGoogle Scholar
  38. Ruspini EH (1970) Numerical methods for fuzzy clustering. Inf Sci 2:319–350CrossRefzbMATHGoogle Scholar
  39. Schilke SW, Bleimann U, Furnell SM, Phippen AD (2004) Multi-dimensional-personalization for location and interest-based recommendation. Internet Res 14(5):379–385CrossRefGoogle Scholar
  40. Shin W, Lin T (2016) Who avoids location-based advertising and why? Investigating the relationship between user perceptions and advertising avoidance. Comput Hum Behav 63(2016):444–452CrossRefGoogle Scholar
  41. Song HY, Choi DY (2015) Defining measures for location visiting preference. Proc Comput Sci 63:142–147CrossRefGoogle Scholar
  42. Sowmya B, Rani BS (2011) Colour image segmentation using fuzzy clustering techniques and competitive neural network. Appl Soft Comput 11(3):3170–3178CrossRefGoogle Scholar
  43. Sun Y, Fan H, Bakillah M, Zipf A (2013) Road-based travel recommendation using geo-tagged images. Comput Environ Urban Syst. Google Scholar
  44. Torra V, Miyamoto S, Lanau S (2005) Exploration of textual document archives using a fuzzy hierarchical clustering algorithm in the GAMBAL system. Inf Process Manag 41(3):587–598CrossRefzbMATHGoogle Scholar
  45. Versichele M, De Groote L, Bouuaert MC, Neutens T, Moerman I, Van de Weghe N (2014) Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: a case study of Ghent, Belgium. Tour Manag 44:67–81CrossRefGoogle Scholar
  46. Vu THN, Ryu KH, Park N (2009) A method for predicting future location of mobile user for location-based services system. Comput Ind Eng 57:91–105CrossRefGoogle Scholar
  47. Yang WS, Cheng HC, Dia JB (2008) A location-aware recommender system for mobile shopping environments. Expert Syst Appl 34:437–445CrossRefGoogle Scholar
  48. Zou X, Huang KW (2015) Leveraging location-based services for couponing and infomediation. Decis Support Syst 78:93–103CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Industrial Engineering Department, Faculty of ManagementIstanbul Technical UniversityMacka, IstanbulTurkey

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