Selecting target market by similar measures in interval intuitionistic fuzzy set
Abstract
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
Keyword : intuitionistic fuzzy set, interval – valued intuitionistic fuzzy set, similarity measure, target market, market segment
How to Cite
Thao, N. X., & Duong, T. T. T. (2019). Selecting target market by similar measures in interval intuitionistic fuzzy set. Technological and Economic Development of Economy, 25(5), 934-950. https://doi.org/10.3846/tede.2019.10290
This work is licensed under a Creative Commons Attribution 4.0 International License.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Simkin, L., & Dibb, S. (1998). Prioritizing target markets. Marketing Intelligence and Planning, 16, 407417. 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Winter, F. W. (1979). A cost-benefit approach to market segmentation. Journal of Marketing, 43, 103-111.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Simkin, L., & Dibb, S. (1998). Prioritizing target markets. Marketing Intelligence and Planning, 16, 407417. 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Winter, F. W. (1979). A cost-benefit approach to market segmentation. Journal of Marketing, 43, 103-111.