1. Reingold, S. C. 2002.Theoretical Concepts, Current Research, and Clinical Trials in Multiple Sclerosis. Comprehensive Nursing Care in Multiple Sclerosis 2002; 15.
2. Horn, D. 2001. Clustering via Hilbert space. Physica A: Statistical Mechanics and its Applications 2001; 302: 70–79.
3. Jain, A. K. 2009. Data clustering: 50 years beyond k-means. Pattern Recognition Letters 2009; 31(8): 651–666.
4. Zadeh, L. A. 1965. Fuzzy sets. Information and Control 1965; 8: 338–353.
5. Atanassov, KT. 1986. Intuitionistic fuzzy sets. Fuzzy Sets and Systems 1986; 20: 87–96.
6. Torra, V. 2010. Hesitant fuzzy sets. International Journal of Intelligent Systems 2010; 25: 529–539.
7. Ruspini, ER. 1969. A new approach to clustering. Information and Control 1969; 15(1): 22–32.
8. Dunn, J.C. 1973. A fuzzy relative of the isodata process and its uses in detecting compact well-separated clusters. Journal of Cybernetics 1973; 32–57.
9. Bezdek, J.C. & Dunn, J.C. 1986. Optimal fuzzy partitions; a heuristic for estimating the parameters in a mixture of normal distributions. IEEE Transactions on Computers 1986; C-35(8): 935–938.
10. فرنام، م. و درهمیرکی، م. 1401. خوشهبندی دادههای فازی با به کارگیری الگوریتم FCM بر مبنای یک معیار اندازه فاصله پارامتری. سیستمهای فازی و کاربردها. 5 (2): 119–93.
11. Zhi, XB., Fan, JL. & Zhao, F. 2014. Robust local feature weighting hard c-means clustering algorithm. Neurocomputing 2014; 134: 20–29.
12. Wang, X., Wang, Y., & Wang, L. 2004. Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognition Letters 2004; 25: 1123–1132.
13. Xing, HJ., & Ha, MH. 2014. Further improvements in Feature-Weighted Fuzzy C-Means. Information Sciences 2014; 267: 1–15.
14. Wu, KL., & Yang, MS. 2002. Alternative c-means clustering algorithms. Pattern Recognition 2002; 35: 2267–2278.
15. Branco, D., Martino, BD., Esposito, A., Tedeschi, G., Bonavita, S., & Lavorgna, L. 2022. Machine learning techniques for prediction of multiple sclerosis progression. Soft Computing 2022; 26(22): 12041–12055.
16. Eshaghi, A., Young, AL., Wijeratne, PA., et al. 2021. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nature Communications 2021; 12: 2078.
17. Huang, Z. 1998. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 1998; 2(3): 283–304.
18. Ji, J., Pang, W., Zhou, C., Han, X., & Wang, Z. 2012. A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data. Knowledge-Based Systems 2012; 30: 129–135.
19. Barjui, SP., Reiisi, S., & Bayati, A. 2017. Human glutathione s-transferase enzyme gene variations and risk of multiple sclerosis in Iranian population cohort. Multiple Sclerosis and Related Disorders 2017; 17: 41–46.
20. Bezdek, JC.1981. Pattern Recognition with Fuzzy Objective Function Algorithms. 1st Edition, Plenum Press: New York.
21. Gustafson, DE. 1979. Kessel W. Fuzzy clustering with a fuzzy covariance matrix. Proceedings of IEEE Conference on Decision and Control, San Diego 1979; 761–766.
22. Bai, L., Liang, JY., & Dang, CY. 2011. An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data. Knowledge-Based Systems 2011; 24(6): 785–795.
23. Wijaya, YA., & Kurniady, DA. 2021. Setyanto E, Tarihoran WS, Rusmana D, Rahim R. Davies Bouldin index algorithm for optimizing clustering case studies mapping school facilities. TEM Journal 2021; 10(3): 1099–1103.
24. Ontaneda, D., Thompson, AJ., Fox, RJ., & Cohen, JA. 2017. Progressive multiple sclerosis: prospects for disease therapy, repair, and restoration of function. The Lancet 2017; 389: 1357-1366.
25. Weideman, AM., Tapia-Maltos, MA., Johnson, K., Greenwood, M., & Bielekova, B. 2017. Meta-analysis of the age-dependent efficacy of multiple sclerosis treatments. Frontiers in Neurology 2017; 8: 577.
26. He, A., Spelman, T., & Manouchehrinia, A. 2023. Ciccarelli O, Hillert J, McKay K. Association between early treatment of multiple sclerosis and patient-reported outcomes: a nationwide observational cohort study. Journal of Neurology, Neurosurgery & Psychiatry 2023; 94: 284-289.
27. Lublin, FD., Reingold, SC., Cohen, JA., Cutter, GR., Sørensen, PS., Thompson, AJ., ... Polman, CH. 2014. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 2014; 83: 278-286.
28. Hult, KJ. 2017. Measuring the potential health impact of personalized medicine: evidence from MS treatments. National Bureau of Economic Research 2017; No. w23900
