Selecting target market by similar measures in interval intuitionistic fuzzy set
DOI: https://doi.org/10.3846/tede.2019.10290Abstract
The selection of the target market plays vital role in promoting the marketing strategies of companies. We presented is a method for target market selection. We introduce some novel similarity measures between intuitionistic fuzzy sets and the novel similarity measures between interval-valued intuitionistic fuzzy sets. They are constructed by combining exponential and other functions. Finally, we introduce a multi-criteria decision making model to select target market by using the novel similarity measure of interval intuitionistic fuzzy sets.
First published online 21 June 2019
Keywords:
intuitionistic fuzzy set, interval – valued intuitionistic fuzzy set, similarity measure, target market, market segmentHow to Cite
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Copyright (c) 2019 The Author(s). Published by Vilnius Gediminas Technical University.
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References
Aghdaie, M. H., Zolfani, S. H., & Zavadskas, E. K. (2013). Market segment evaluation and selection based on application of fuzzy AHP and COPRAS-G methods. Journal of Business Economics and Management, 14, 213-233. https://doi.org/10.3846/16111699.2012.721392"> https://doi.org/10.3846/16111699.2012.721392
Aghdaie, M. H. (2015). Target market selection based on market segment evaluation: a multiple attribute decision making approach. International Journal Operational Research, 24, 262-278. https://doi.org/10.1504/IJOR.2015.072231"> https://doi.org/10.1504/IJOR.2015.072231
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3"> https://doi.org/10.1016/S0165-0114(86)80034-3
Atanassov, K. T., & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31(3), 343-349. https://doi.org/10.1016/0165-0114(89)90205-4"> https://doi.org/10.1016/0165-0114(89)90205-4
Bernstein, J. S. (2014). Marketing insights for engaging performing arts audiences. New York: Palgrave Macmillan.
Bharati, S. K., & Singh, S. R. (2014). Intuitionistic fuzzy optimization technique in agricultural production planning: A small farm holder perspective. International Journal of Computer Applications, 89(6), 17-23. https://doi.org/10.5120/15507-4276"> https://doi.org/10.5120/15507-4276
Buhaesku, T. (1988). On the convexity of intuitionistic fuzzy sets. In Itinerant Seminar of Functional Equations, Approximation and Convexity (pp. 137-144). Cluj-Napoca.
Bustince, H., & Burillo, P. (1995). Correlation of interval-valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 74(2), 237-244. https://doi.org/10.1016/0165-0114(94)00343-6"> https://doi.org/10.1016/0165-0114(94)00343-6
Chiang, D. A., & Lin, N. P. (1999). Correlation of fuzzy sets. Fuzzy Sets and Systems, 102(2), 221-226. https://doi.org/10.1016/S0165-0114(97)00127-9"> https://doi.org/10.1016/S0165-0114(97)00127-9
Chiu, C.-Y., Chen, Y.-F., & Kuo, I.-T. K. (2009). An intelligent market segmentation system using kmeans and particle swarm optimization. Expert Systems with Applications, 36, 4558-4565. https://doi.org/10.1016/j.eswa.2008.05.029"> https://doi.org/10.1016/j.eswa.2008.05.029
Gerstenkorn, T., & Mańko, J. (1991). Correlation of intuitionistic fuzzy sets. Fuzzy Sets and Systems, 44(1), 39-43. https://doi.org/10.1016/0165-0114(91)90031-K"> https://doi.org/10.1016/0165-0114(91)90031-K
Hung, W. L. (2001). Using statistical viewpoint in developing correlation of intuitionistic fuzzy sets. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(4), 509-516. https://doi.org/10.1142/S0218488501000910"> https://doi.org/10.1142/S0218488501000910
Hung, W. L., & Wu, J. W. (2002). Correlation of intuitionistic fuzzy sets by centroid method. Information Sciences, 144(1), 219-225. https://doi.org/10.1016/S0020-0255(02)00181-0"> https://doi.org/10.1016/S0020-0255(02)00181-0
Hung, W. L., & Yang, M. S. (2004). Similarity measure of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recognition Letters, 25, 1603-1611. https://doi.org/10.1016/j.patrec.2004.06.006"> https://doi.org/10.1016/j.patrec.2004.06.006
Hwang, C. M., Yang, M. S., Hung, W. L., & Lee, M. G. (2012). A similarity measure of intuitionistic fuzzy sets based on the Sugeno integral with its application to pattern recognition. Information Sciences, 189, 93-109. https://doi.org/10.1016/j.ins.2011.11.029"> https://doi.org/10.1016/j.ins.2011.11.029
Kelemenis, A., & Askounis, D. (2010). A new TOPSIS-based multi-criteria approach for personal selection. Expert Systems with Applications, 37, 4999-5008. https://doi.org/10.1016/j.eswa.2009.12.013"> https://doi.org/10.1016/j.eswa.2009.12.013
Kotler, P., & Armstrong, G. (2003). Principles of marketing (10th ed.). Upper Saddle River, NJ: PrenticeHall.
Kotler, P. (1980). Marketing management – analysis, planning, and control (4th ed.). Upper Saddle River, NJ: Prentice-Hall.
Kuo, R. J., Ho, L. M., & Hu, C. M. (2002). Integration of self-organizing feature map and K-meansalgorithm for market segmentation. Computers and Operations Research, 29, 1475-1493. https://doi.org/10.1016/S0305-0548(01)00043-0"> https://doi.org/10.1016/S0305-0548(01)00043-0
Li, D., & Cheng, C. (2002). New similarity measures of intuitionistic fuzzy sets and application to pattern recognition. Pattern Recognition Letters, 23, 221-225. https://doi.org/10.1016/S0167-8655(01)00110-6"> https://doi.org/10.1016/S0167-8655(01)00110-6
Li, J., & Zeng, W. (2015). A new dissimilarity measure between intuitionistic fuzzy sets and its application in multiple attribute decision making. Journal of Intelligent & Fuzzy Systems, 29(4), 1311-1320. https://doi.org/10.3233/IFS-141440"> https://doi.org/10.3233/IFS-141440
Liang, Z., & Shi, P. (2003). Similarity measures on intuitionistic fuzzy sets. Pattern Recognition Letters, 24(15), 2687-2693. https://doi.org/10.1016/S0167-8655(03)00111-9"> https://doi.org/10.1016/S0167-8655(03)00111-9
Liu, B., Shen, Y., Mu, L., Chen, X., & Chen, L. (2016). A new correlation measure of the intuitionistic fuzzy sets. Journal of Intelligent & Fuzzy Systems, 30(2), 1019-1028. https://doi.org/10.3233/IFS-151824"> https://doi.org/10.3233/IFS-151824
Mitchell, H. B. (2004). A correlation coefficient for intuitionistic fuzzy sets. International Journal of Intelligent Systems, 19(5), 483-490. https://doi.org/10.1002/int.20004"> https://doi.org/10.1002/int.20004
Nadler Jr., S. B. (1978). Hyperspaces of sets. New York: Marcel Dekker.
Pal, N. R., & Pal, S. K. (1992). Some properties of the exponential entropy. Information Sciences, 66, 119-137. https://doi.org/10.1016/0020-0255(92)90090-U"> https://doi.org/10.1016/0020-0255(92)90090-U
Park, J. H., Hwang, J. H., Park, W. J., Wei, H., & Lee, S. H. (2013). Similarity measure on intuitionistic fuzzy sets. Journal of Central South University, 20(8), 2233-2238. https://doi.org/10.1007/s11771-013-1729-y"> https://doi.org/10.1007/s11771-013-1729-y
Simkin, L., & Dibb, S. (1998). Prioritizing target markets. Marketing Intelligence and Planning, 16, 407417. https://doi.org/10.1108/02634509810244417"> https://doi.org/10.1108/02634509810244417
Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., & Diabat, A. (2013). A fuzzy multi criteria approach for evaluating green supplier’s performance in green supply chain with linguistic preferences. Resources, Conservation and Recycling, 74, 170-179. https://doi.org/10.1016/j.resconrec.2012.09.006"> https://doi.org/10.1016/j.resconrec.2012.09.006
Shi, L. L., & Ye, J. (2013). Study on fault diagnosis of turbine using an improved cosine similarity measure for vague sets. Journal of Applied Sciences, 13(10), 1781-1786. https://doi.org/10.3923/jas.2013.1781.1786"> https://doi.org/10.3923/jas.2013.1781.1786
Shidpour, H., Bernard, A., & Shahrokhi, M. (2013). A group decision-making method based on intuitionistic fuzzy set in the three dimensional concurrent engineering environment: A multi-objective programming approach. Procedia CIRP, 7, 533-538. https://doi.org/10.1016/j.procir.2013.06.028"> https://doi.org/10.1016/j.procir.2013.06.028
Phong, P. H., & Son, L. H. (2017). Linguistic vector similarity measures and applications to linguistic information classification. International Journal of Intelligent System, 32, 67-81. https://doi.org/10.1002/int.21830"> https://doi.org/10.1002/int.21830
Szmidt, E., & Kacprzyk, J. (1996). Intuitionistic fuzzy sets in group decision making. Notes on IFS, 2(1), 11-14.
Szmidt, E., & Kacprzyk, J. (2004). A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. In International Conference on Artificial Intelligence and Soft Computing (ICAISC 2004) (pp. 388-393). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-24844-6_56"> https://doi.org/10.1007/978-3-540-24844-6_56
Xu, Z. S. (2006). On correlation measures of intuitionistic fuzzy sets. Lecture Notes in Computer Science, 4224, 16-24. https://doi.org/10.1007/11875581_2"> https://doi.org/10.1007/11875581_2
Xu, Z. S. (2007a). Method for aggregation interval-valued intuitionistic fuzzy information and their application to decision making. Control and Decision, 22(2), 215-219.
Xu, Z. S. (2007b). Some similarity measures of intuitionistic fuzzy sets and their applications to multiple attribute decision making. Fuzzy Optimization and Decision Making, 6(2), 109-121. https://doi.org/10.1007/s10700-007-9004-z"> https://doi.org/10.1007/s10700-007-9004-z
Xu, Z. S. (2010). Choquet integrals of weighted intuitionistic fuzzy information. Information Sciences, 180(5), 726-736. https://doi.org/10.1016/j.ins.2009.11.011"> https://doi.org/10.1016/j.ins.2009.11.011
Xu, Z. S., & Hu, H. (2010). Projection models for intuitionistic fuzzy multiple attribute decision-making. International Journal of Information Technology & Decision-Making, 9(2), 267-280. https://doi.org/10.1142/S0219622010003816"> https://doi.org/10.1142/S0219622010003816
Ye, J. (2011). Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathematical and Computer Modelling, 53, 91-97. https://doi.org/10.1016/j.mcm.2010.07.022"> https://doi.org/10.1016/j.mcm.2010.07.022
Ye, J. (2016). Similarity measures of intuitionistic fuzzy sets based on cosine function for the decision making of mechanical design schemes. Journal of Intelligent & Fuzzy Systems, 30(1), 151-158. https://doi.org/10.3233/IFS-151741"> https://doi.org/10.3233/IFS-151741
Zhou, B. (2016). A new similarity measure of intuitionistic fuzzy sets considering abstention group influence and its applications. Journal of Intelligent Systems, 25(2), 197-208.
Zhou, B., Zhao, R., Yu, F., & Tian, H. (2016). Intuitionistic fuzzy entropy clustering algorithm for infrared image segmentation. Journal of Intelligent & Fuzzy Systems, 30(3), 1831-1840. https://doi.org/10.3233/IFS-151894"> https://doi.org/10.3233/IFS-151894
Zadeh, L. A. (1971). Similarity relations and fuzzy orderings. Information Sciences, 3(2), 177-200.
https://doi.org/10.1016/S0020-0255(71)80005-1"> https://doi.org/10.1016/S0020-0255(71)80005-1
Zeng, W., & Wang, J. (2011). Correlation coefficient of interval-valued intuitionistic fuzzy sets.In International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 98-102). IEEE. https://doi.org/10.1109/FSKD.2011.6019507"> https://doi.org/10.1109/FSKD.2011.6019507
Wei, C. P., Wang, P., & Zhang, Y. Z. (2011). Entropy, similarity measure of interval-valued intuitionistic fuzzy sets and their applications. Information Sciences, 181, 4273-4286. https://doi.org/10.1016/j.ins.2011.06.001"> https://doi.org/10.1016/j.ins.2011.06.001
Winter, F. W. (1979). A cost-benefit approach to market segmentation. Journal of Marketing, 43, 103-111.
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