References
- [1] Anderson E., The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 1, 1935, 2-5.
- [2] Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
- [3] Chiang J.-H., Yue S., Yin Z.-X., A new fuzzy cover approach to clustering, IEEE Transactions on Fuzzy Systems, 12, 2, 2004, 199-208.
- [4] Corsini P., Lazzerini B., Marcelloni F., A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm, Soft Computing, 9, 6, 2005, 439-447.
- [5] De Cáceres M., Oliva F., Font X., On relational possibilistic clustering, Pattern Recognition, 39, 11, 2006, 2010-2024.
- [6] Everitt B.S., Landau S., Leese M., Stahl D., Cluster Analysis, 5th Edition, Wiley, Chichester, 2011.
- [7] Hamasuna Y., Endo Y., Miyamoto S., Fuzzy C-means clustering for data with clusterwise tolerance based on L2- and L1-regularization, Journal of Advanced Computational Intelligence and Intelligent Informatics, 15, 1, 2011, 68-75.
- [8] Höppner F., Klawonn F., Kruse R., Runkler T., Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, John Wiley & Sons, Chichester, 1999.
- [9] Kaufmann A., Introduction to the Theory of Fuzzy Subsets, Academic Press, New York, 1975.
- [10] Komazaki Y., Miyamoto S., Variables for controlling cluster sizes on fuzzy C-means, in: V. Torra, Y. Narukawa, G. Navarro-Arribas, D. Megías (eds.), Modeling Decisions for Artificial Intelligence: Proceedings of the 10th International Conference MDAI’2013, Barcelona, Spain, November 20-22, 2013, Springer, Berlin, 2013, 192-203.
- [11] Krishnapuram R., Keller J.M., A possibilistic approach to clustering, IEEE Transactions on Fuzzy Systems, 1, 2, 1993, 98-110.
- [12] Łęski J.M., Robust possibilistic clustering, Archives of Control Sciences, 10, 3/4, 2000, 141-155.
- [13] Mandel I.D., Clustering Analysis, Finansy i Statistica, Moscow, 1988. (in Russian)
- [14] Ménard M., Courboulay V., Dardignac P.-A., Possibilistic and probabilistic fuzzy clustering: unification within the framework of the non-extensive thermostatistics, Pattern Recognition, 36, 6, 2003, 1325-1342.
- [15] Miyamoto S., Ichihashi H., Honda K., Algorithms for Fuzzy Clustering. Methods in C-Means Clustering with Applications, Springer, Berlin, 2008.
- [16] Miyamoto S., Different objective functions in fuzzy C-means algorithms and kernel-based clustering, International Journal of Fuzzy Systems, 13, 2, 2011, 89-97.
- [17] Pedrycz W., Fuzzy sets in pattern recognition: methodology and methods, Pattern Recognition, 23, 1/2, 1990, 121-146.
- [18] Sato-Ilic M., Jain L.C., Innovations in Fuzzy Clustering. Theory and Applications, Springer, Berlin, 2006.
- [19] Sneath P.H.A., Sokal R., Numerical Taxonomy, Freeman, San Francisco, 1973.
- [20] Tamura S., Higuchi S., Tanaka, K., Pattern classification based on fuzzy relations, IEEE Transactions on Systems, Man, and Cybernetics, 1, 1, 1971, 61-66.
- [21] Vapnik V.N., Statistical Learning Theory, Wiley, New York, 1998.
- [22] Viattchenin D.A., A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications, Springer, Berlin, 2013.
- [23] Viattchenin D.A., Damaratski A., Direct heuristic algorithms of possibilistic clustering based on transitive approximation of fuzzy tolerance, Informatica Economicá Journal, 17, 3, 2013, 5-15.
- [24] Viattchenin D.A., Yaroma A., Damaratski A., A novel direct relational heuristic algorithm of possibilistic clustering, International Journal of Computer Applications, 107, 18, 2014, 15-21.
- [25] Walesiak M., Ugólniona Miara Odległości w Statystycznej Analizie Wielowymiarowej, Wydawnictwo Akademii Ekonomicznej im. Oskara Langego, Wrocław, 2002. (in Polish)
- [26] Xie Z., Wang S.T., Chung F.L., An enhanced possibilistic c-means clustering algorithm EPCM, Soft Computing, 12, 6, 2008, 593-611.
- [27] Yang M.-S., Wu K.-L., Unsupervised possibilistic clustering, Pattern Recognition, 39, 1, 2006, 5-21.