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

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Abstract

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.

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References

  • Agrawal V (2015) Novel fuzzy clustering algorithm for fuzzy data. In: 2015 Eighth international conference on contemporary computing (IC3), 20–22 Aug 2015

  • 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 157–168

  • Anagnostopoulos C, Hadjiefthymiades S, Kolomvatsos K (2015) Time-optimized user grouping in location based services. Comput Netw 81:220–244

    Article  MATH  Google Scholar 

  • Chen N, Xu ZS, Xia MM (2014) Hierarchical hesitant fuzzy K-means clustering algorithm. Appl Math A J Chin Univ 29:1–17

    Article  MathSciNet  MATH  Google 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 2000

  • Coppi R, D’Urso P, Giordani P (2012) Fuzzy and possibilistic clustering for fuzzy data. Comput Stat Data Anal 56(4):915–927

    Article  MathSciNet  MATH  Google Scholar 

  • D’Urso P, Giordani P (2006) A weighted fuzzy c-means clustering model for fuzzy data. Comput Stat Data Anal 50(6):1496–1523

    Article  MathSciNet  MATH  Google Scholar 

  • 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 73:335–346

    Article  Google Scholar 

  • Devi MU, Gandhi GM (2015) An enhanced fuzzy clustering and expectation maximization framework based matching semantically similar sentences. Proc Comput Sci 57:1149–1159

    Article  Google Scholar 

  • 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):2248–2252

    Article  Google Scholar 

  • 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):28–32

    Article  Google Scholar 

  • Fu P, Yin H (2012) Logistics enterprise evaluation model based on fuzzy clustering analysis. Phys Proc 24(Part C):1583–1587

    Article  Google Scholar 

  • 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):1299–1305

    Article  MATH  Google Scholar 

  • Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333

    Article  Google Scholar 

  • Gosain A, Dahiya S (2016) Performance analysis of various fuzzy clustering algorithms: a review. Proc Comput Sci 79:100–111

    Article  Google Scholar 

  • Han L, Chen G (2009) A fuzzy clustering method of construction of ontology-based user profiles. Adv Eng Softw 40(7):535–540

    Article  MATH  Google Scholar 

  • Han J, Kamber M (2001) Data mining concepts and techniques. Morgan Kauffman Publishers, Burlington, pp 5–33

    Google Scholar 

  • Hipp J, Güntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explor Newsl 2(1):58–64

    Article  Google Scholar 

  • 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 87:91–103

    Article  Google Scholar 

  • Junglas IA, Watson RT (2008) Location-based services. Commun ACM 51(3):65–69

    Article  Google Scholar 

  • Kuo MH, Chen LC, Liang CW (2009) Building and evaluating a location-based service recommendation system with a preference adjustment mechanism. Expert Syst Appl 36:3543–3554

    Article  Google 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 21–30

  • 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 51:336–343

    Article  Google Scholar 

  • Li K, Du TC (2012) Building a targeted mobile advertising system for location-based services. Decis Support Syst 54(1):1–8

  • 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):257–270

    Google Scholar 

  • Li YM, Chou CL, Lin LF (2014) A social recommender mechanism for location-based group commerce. Inf Sci 274:125–142

    Article  Google Scholar 

  • Lin TTC, Paragas F, Goh D, Bautista JR (2016) Developing location-based mobile advertising in Singapore: a socio-technical perspective. Technol Forecast Soc Change 103:334–349

    Article  Google Scholar 

  • Mendel JM, John RB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  Google Scholar 

  • Mobile Marketing Association (2011) Mobile location based services marketing whitepaper. Technical Report. Mobile Marketing Association

  • 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 61–80

    Chapter  Google 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 21–39

  • Park DH, Kim HK, Choi Y, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39:10059–10072

    Article  Google Scholar 

  • Pingley A, Yu W, Zhang N, Fu X, Zhao W (2012) A context-aware scheme for privacy-preserving location-based services. Comput Netw 56:2551–2568

    Article  Google 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):252–8

    Google Scholar 

  • 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):134–148

    Article  Google Scholar 

  • Rodriguez RM, Martinez L, Herrera F (2012) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119

    Article  Google Scholar 

  • 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 241:28–42

    Article  MathSciNet  MATH  Google Scholar 

  • Ruspini EH (1970) Numerical methods for fuzzy clustering. Inf Sci 2:319–350

    Article  MATH  Google Scholar 

  • Schilke SW, Bleimann U, Furnell SM, Phippen AD (2004) Multi-dimensional-personalization for location and interest-based recommendation. Internet Res 14(5):379–385

    Article  Google Scholar 

  • 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):444–452

    Article  Google Scholar 

  • Song HY, Choi DY (2015) Defining measures for location visiting preference. Proc Comput Sci 63:142–147

    Article  Google Scholar 

  • Sowmya B, Rani BS (2011) Colour image segmentation using fuzzy clustering techniques and competitive neural network. Appl Soft Comput 11(3):3170–3178

    Article  Google 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 

  • 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):587–598

    Article  MATH  Google Scholar 

  • 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 44:67–81

    Article  Google Scholar 

  • Vu THN, Ryu KH, Park N (2009) A method for predicting future location of mobile user for location-based services system. Comput Ind Eng 57:91–105

    Article  Google Scholar 

  • Yang WS, Cheng HC, Dia JB (2008) A location-aware recommender system for mobile shopping environments. Expert Syst Appl 34:437–445

    Article  Google Scholar 

  • Zou X, Huang KW (2015) Leveraging location-based services for couponing and infomediation. Decis Support Syst 78:93–103

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Industrial Engineering Department, Faculty of Management, Istanbul Technical University, 34367, Macka, Istanbul, Turkey

    Sultan Ceren Oner & Başar Oztaysi

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  1. Sultan Ceren Oner
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  2. Başar Oztaysi
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Correspondence to Sultan Ceren Oner.

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Communicated by C. Kahraman.

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Oner, S.C., Oztaysi, B. An interval type 2 hesitant fuzzy MCDM approach and a fuzzy c means clustering for retailer clustering. Soft Comput 22, 4971–4987 (2018). https://doi.org/10.1007/s00500-018-3191-0

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