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Fuzzy data analysis and classification. Special issue in memoriam of Professor Lotfi A. Zadeh, father of fuzzy logic. (English) Zbl 1476.00115

From the text: This special issue started in 2015, with the 50th anniversary of the seminal paper on fuzzy sets by L. A. Zadeh [Inf. Control 8, 338–353 (1965; Zbl 0139.24606)], aiming to collect a sample of research papers about the current trends on the combination of fuzzy sets/logic and data analysis/classification.

MSC:

00B30 Festschriften
00B15 Collections of articles of miscellaneous specific interest
62-06 Proceedings, conferences, collections, etc. pertaining to statistics
62A86 Fuzzy analysis in statistics

Biographic References:

Zadeh, Lofti A.

Citations:

Zbl 0139.24606

Software:

FCM4DD
Full Text: DOI

References:

[1] Ansari ZA, Sattar SA, Babu AV (2017) A fuzzy neural network based framework to discover user access patterns from web log data. Adv Data Anal Classif 11(3):519-546 · Zbl 1414.82024 · doi:10.1007/s11634-015-0228-4
[2] Arnold BF (1998) Testing fuzzy hypotheses with crisp data. Fuzzy Sets Syst 94(3):323-333 · Zbl 0940.62015 · doi:10.1016/S0165-0114(96)00258-8
[3] Aşan Z, Greenacre M (2011) Biplots of fuzzy coded data. Fuzzy Sets Syst 183:57-71 · doi:10.1016/j.fss.2011.03.007
[4] Auephanwiriyakul S, Keller JM (2002) Analysis and efficient implementation of a linguistic fuzzy c-means. IEEE Trans Fuzzy Syst 10:563-582 · doi:10.1109/TFUZZ.2002.803492
[5] Bandemer H, Näther W (1992) Fuzzy data analysis. Springer, Dordrecht · Zbl 0758.62003 · doi:10.1007/978-94-011-2506-2
[6] Bellman RE, Kalaba R, Zadeh LA (1966) Abstraction and pattern classification. J Math Anal Appl 13:1-7 · Zbl 0134.15305 · doi:10.1016/0022-247X(66)90071-0
[7] Belohlavek R, Dauben JW, Klir GJ (2017) Fuzzy logic and mathematics. A historical perspective. Oxford University Press, New York · Zbl 1400.03004 · doi:10.1093/oso/9780190200015.001.0001
[8] Berlinger J, Hüllermeier E (2007) Fuzzy clustering of parallel data streams. In: De Oliveira and Pedrycz (2007), pp 333-352
[9] Bezdek JC (1973) Cluster validity with fuzzy sets. Cybern Syst/J Cybern 3(3):58-73 · Zbl 0294.68035
[10] Bezdek JC (1974) Numerical taxonomy with fuzzy sets. J Math Biol 1(1):57-71 · Zbl 0403.62039 · doi:10.1007/BF02339490
[11] Bezdek JC (1980) Convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans Pattern Anal Mach Intell 2(1):1-8 · Zbl 0441.62055 · doi:10.1109/TPAMI.1980.4766964
[12] Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York · Zbl 0503.68069 · doi:10.1007/978-1-4757-0450-1
[13] Bezdek JC, Ehrlich R, Full W (1984) FCM—the fuzzy c-means clustering-algorithm. Comput Geosci 10(2-3):191-203 · doi:10.1016/0098-3004(84)90020-7
[14] Blanco-Fernández A, Casals MR, Colubi A, Corral N, García-Bárzana M, Gil MA, González-Rodríguez G, López MT, Lubiano MA, Montenegro M, Ramos-Guajardo AB, de la Rosa de Sáa S, Sinova B (2014a) A distance-based statistical analysis of fuzzy number-valued data. Int J Approx Reason 55(7):1487-1501 · Zbl 1407.62094 · doi:10.1016/j.ijar.2013.09.020
[15] Blanco-Fernández A, Casals MR, Colubi A, Corral N, García-Bárzana M, Gil MA, González-Rodríguez G, López MT, Lubiano MA, Montenegro M, Ramos-Guajardo AB, de la Rosa de Sáa S, Sinova B (2014b) Rejoinder on “A distance-based statistical analysis of fuzzy number-valued data”. Int J Approx Reason 55(7):1601-1605 · Zbl 1407.62095 · doi:10.1016/j.ijar.2014.04.003
[16] Buckley JJ (2004) Fuzzy statistics. Studies in fuzziness and soft computing series 149. Springer, Berlin · Zbl 1076.62029
[17] Calcagnì A, Lombardi L, Pascali E (2016) A dimension reduction technique for two-mode non-convex fuzzy data. Soft Comput 20:749-762 · doi:10.1007/s00500-014-1538-8
[18] Cappelli C, D’Urso P, Di Iorio F (2013) Change point analysis for imprecise time series. Fuzzy Sets Syst 225:23-38 · Zbl 1284.62594 · doi:10.1016/j.fss.2013.03.001
[19] Celminš A (1987) Multidimensional least-squares fitting of fuzzy models. Math Model 9:669-690 · Zbl 0636.62111 · doi:10.1016/0270-0255(87)90468-4
[20] Chen N, Xu Z, Xia M (2013) Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis. Appl Math Model 37:2197-2211 · Zbl 1349.62293 · doi:10.1016/j.apm.2012.04.031
[21] Colubi A, González-Rodríguez G, Gil MA, Trutschnig W (2011) Nonparametric criteria for supervised classification of fuzzy data. Int J Approx Reason 52:1272-1282 · Zbl 1319.62125 · doi:10.1016/j.ijar.2011.05.007
[22] Coppi R, D’Urso P (2002) Fuzzy k-means clustering models for triangular fuzzy time trajectories. Stat Methods Appl 11(1):21-40 · Zbl 1145.62347 · doi:10.1007/BF02511444
[23] Coppi R, D’Urso P (2003) Three-way fuzzy clustering models for LR fuzzy time trajectories. Comput Stat Data Anal 43:149-177 · Zbl 1429.62273 · doi:10.1016/S0167-9473(02)00226-8
[24] Coppi R, D’Urso P (2006) Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization. Comput Stat Data Anal 50:1452-1477 · Zbl 1445.62156 · doi:10.1016/j.csda.2005.01.008
[25] Coppi R, D’Urso P, Giordani P, Santoro A (2006) Least squares estimation of a linear regression model with LR fuzzy response. Comput Stat Data Anal 51:267-286 · Zbl 1157.62460 · doi:10.1016/j.csda.2006.04.036
[26] Coppi R, D’Urso P, Giordani P (2010) A fuzzy clustering model for multivariate spatial time series. J Classif 27:54-88 · Zbl 1337.62305 · doi:10.1007/s00357-010-9043-y
[27] Coppi R, D’Urso P, Giordani P (2012) Fuzzy and possibilistic clustering for fuzzy data. Comput Stat Data Anal 56(4):915-927 · Zbl 1243.62089 · doi:10.1016/j.csda.2010.09.013
[28] Couso I, Dubois D (2014) Statistical reasoning with set-valued information: ontic vs. epistemic views. Int J Approx Reason 55(7):1502-1518 · Zbl 1407.62032 · doi:10.1016/j.ijar.2013.07.002
[29] D’Urso P (2003) Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data. Comput Stat Data Anal 42(1-2):47-72 · Zbl 1429.62337 · doi:10.1016/S0167-9473(02)00117-2
[30] D’Urso P (2005) Fuzzy clustering for data time array with inlier and outlier time trajectories. IEEE Trans Fuzzy Syst 13:583-604 · doi:10.1109/TFUZZ.2005.856565
[31] D’Urso P (2007) Fuzzy clustering of fuzzy data. In: De Oliveira and Pedrycz (2007), pp 155-192
[32] D’Urso, P.; Hennig, C. (ed.); Meila, M. (ed.); Murtagh, F. (ed.); Rocci, R. (ed.), Fuzzy clustering, 545-573 (2016), Boca Raton · Zbl 1396.62161
[33] D’Urso P (2017a) Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: a review. Inform Sci 400-401:30-62 · doi:10.1016/j.ins.2017.03.001
[34] D’Urso P (2017b) Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review. Granul Comput 2:225-247 · doi:10.1007/s41066-017-0040-y
[35] D’Urso P, De Giovanni L (2014) Robust clustering of imprecise data. Chem Intel Lab Syst 136:58-80 · doi:10.1016/j.chemolab.2014.05.004
[36] D’Urso P, Gastaldi T (2000) A least-squares approach to fuzzy linear regression analysis. Comput Stat Data Anal 34:427-440 · Zbl 1046.62066 · doi:10.1016/S0167-9473(99)00109-7
[37] D’Urso P, Giordani P (2005) A possibilistic approach to latent component analysis for symmetric fuzzy data. Fuzzy Sets Syst 150:285-305 · Zbl 1058.62050 · doi:10.1016/j.fss.2004.03.024
[38] D’Urso P, Giordani P (2006) A robust fuzzy k-means clustering model for interval valued data. Comput Stat 21:251-269 · Zbl 1113.62076 · doi:10.1007/s00180-006-0262-y
[39] D’Urso P, Leski J (2016) Fuzzy C-ordered medoids clustering of interval-valued data. Pattern Recogn 58:49-67 · doi:10.1016/j.patcog.2016.04.005
[40] D’Urso P, Massari R (2013) Fuzzy clustering of human activity patterns. Fuzzy Sets Syst 215:29-54 · doi:10.1016/j.fss.2012.05.009
[41] D’Urso P, Santoro A (2006) Fuzzy clusterwise regression analysis with symmetrical fuzzy output variable. Comput Stat Data Anal 51:287-313 · Zbl 1157.62461 · doi:10.1016/j.csda.2006.06.001
[42] D’Urso P, Maharaj EA, Galagedera DUA (2010) Wavelets-based fuzzy clustering of time series. J Classif 27:231-275 · Zbl 1337.62307 · doi:10.1007/s00357-010-9058-4
[43] D’Urso P, Massari R, Santoro A (2011) Robust fuzzy regression analysis. Inform Sci 181:4154-4174 · Zbl 1242.62073 · doi:10.1016/j.ins.2011.04.031
[44] D’Urso P, De Giovanni L, Massari R (2014) Self-organizing maps for imprecise data. Fuzzy Sets Syst 237:63-89 · Zbl 1315.68206 · doi:10.1016/j.fss.2013.09.011
[45] D’Urso P, De Giovanni L, Massari R (2015a) Trimmed fuzzy clustering for interval-valued data. Adv Data Anal Classif 8(1):21-40 · Zbl 1414.62242 · doi:10.1007/s11634-014-0169-3
[46] D’Urso P, De Giovanni L, Massari R (2015b) Time series clustering by a robust autoregressive metric with application to air pollution. Chemom Intel Lab Syst 141(15):107-124 · doi:10.1016/j.chemolab.2014.11.003
[47] D’Urso P, De Giovanni L, Massari R (2016) GARCH-based robust fuzzy clustering of time series. Fuzzy Sets Syst 305:1-28 · Zbl 1368.62167 · doi:10.1016/j.fss.2016.01.010
[48] D’Urso P, De Giovanni L, Massari R, Cappelli C (2017a) Exponential distance-based fuzzy clustering for interval-valued data. Fuzzy Optim Decis Mak 16:51-70 · Zbl 1428.62306 · doi:10.1007/s10700-016-9238-8
[49] D’Urso P, Maharaj EA, Alonso AM (2017b) Fuzzy clustering of time series using extremes. Fuzzy Sets Syst 318:56-79 · Zbl 1381.62162 · doi:10.1016/j.fss.2016.10.006
[50] Davé RN (1991) Characterization and detection of noise in clustering. Pattern Recogn Lett 12:657-664 · doi:10.1016/0167-8655(91)90002-4
[51] De la Rosa de Sáa S, Gil MA, González-Rodríguez G, López MT, Lubiano MA (2016) Fuzzy rating scale-based questionnaires and their statistical analysis. IEEE Trans Fuzzy Syst 23(1):111-126 · doi:10.1109/TFUZZ.2014.2307895
[52] De Oliveira JV, Pedrycz W (2007) Advances in fuzzy clustering and its applications. Wiley, Chichester · doi:10.1002/9780470061190
[53] Denoeux T (2011) Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets Syst 183:72-91 · Zbl 1239.62017 · doi:10.1016/j.fss.2011.05.022
[54] Denoeux T, Masson MH (2000) Multidimensional scaling of interval-valued dissimilarity data. Pattern Recogn Lett 21:83-92 · doi:10.1016/S0167-8655(99)00135-X
[55] Denoeux T, Masson MH (2004a) EVCLUS: evidential clustering of proximity data. IEEE Trans Syst Man Cybern Part B-Cybern 34:95-109 · doi:10.1109/TSMCB.2002.806496
[56] Denoeux T, Masson MH (2004b) Principal component analysis of fuzzy data using autoassociative neural networks. IEEE Trans Fuzzy Syst 12:336-349 · doi:10.1109/TFUZZ.2004.825990
[57] Diamond, P., No article title, Fuzzy least squares. Inform Sci, 46, 141-157 (1988) · Zbl 0663.65150 · doi:10.1016/0020-0255(88)90047-3
[58] Disegna M, D’Urso P, Durante F (2017) Copula-based fuzzy clustering of spatial time series. Spat Stat 21:209-225 · doi:10.1016/j.spasta.2017.07.002
[59] Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Cybern Syst/J Cybern 3(3):32-57 · Zbl 0291.68033
[60] Dunn JC (1974) Well-separated clusters and optimal fuzzy partitions. Cybern Syst/J Cybern 4(1):95-104 · Zbl 0304.68093
[61] Esogbue AO (1986) Optimal clustering of fuzzy data via fuzzy dynamic programming. Fuzzy Sets Syst 18(3):283-298 · Zbl 0616.62088 · doi:10.1016/0165-0114(86)90007-2
[62] Féron R (1979) Sur les notions de distance et d’ecart dans une structure floue et leurs applications aux ensembles aléatoires flous. C R Acad Sci Paris A 289:35-38 · Zbl 0419.60007
[63] Ferraro MB, Giordani P (2017) Possibilistic and fuzzy clustering methods for robust analysis of non-precise data. Int J Approx Reason 88:23-38 · Zbl 1418.68204 · doi:10.1016/j.ijar.2017.05.002
[64] Ferraro, MB; Vichi, M.; Grzegorzewski, P. (ed.); Gagolewski, M. (ed.); Hryniewicz, O. (ed.); Gil, MA (ed.), Fuzzy double clustering: a robust proposal, 225-232 (2015), Cham · Zbl 1337.62147
[65] Ferraro MB, Colubi A, González-Rodríguez G, Coppi R (2011) A determination coefficient for a linear regression model with imprecise response. Environmetrics 22(4):516-529 · doi:10.1002/env.1056
[66] Fréchet M (1948) Les éléments aléatoires de nature quelconque dans un espace distancié. Ann L’Inst H Poincaré 10:215-310 · Zbl 0035.20802
[67] Frigui H, Krishnapuram R (1996) A robust algorithm for automatic extraction of an unknown number of clusters from noisy data. Pattern Recogn Lett 17:1223-1232 · Zbl 0872.68163 · doi:10.1016/0167-8655(96)00080-3
[68] Fritz, H.; García-Escudero, LA; Mayo-Iscar, A., No article title, Robust constrained fuzzy clustering. Inform Sci, 245, 38-52 (2013) · Zbl 1321.62070
[69] Gil MA (1992) Sufficiency and fuzziness in random experiments. Ann Inst Stat Math 44(3):451-462 · Zbl 0763.62003
[70] Gil MA, Jain P (1992) Comparison of experiments in statistical decision problems with fuzzy utilities. IEEE Trans Syst Man Cybern 22(4):662-670 · Zbl 0768.62004 · doi:10.1109/21.156579
[71] Gil MA, López-Díaz M (1996) Fundamentals and Bayesian analyses of decision problems with fuzzy-valued utilities. Int J Approx Reason 15(3):203-224 · Zbl 0949.91504 · doi:10.1016/S0888-613X(96)00073-4
[72] Gil MA, Corral N, Gil P (1988) The minimum inaccuracy estimates in \[\chi^2\] χ2 tests for goodness of fit with fuzzy observations. J Stat Plan Inference 19(1):95-115 · Zbl 0665.62009 · doi:10.1016/0378-3758(88)90055-9
[73] Gil MA, López-Díaz M, López-García H (1998) The fuzzy hyperbolic inequality index associated with fuzzy random variables. Eur J Oper Res 110(2):377-391 · Zbl 0938.91044 · doi:10.1016/S0377-2217(97)00252-X
[74] Gil MA, Montenegro M, González-Rodríguez G, Colubi A, Casals MR (2006) Bootstrap approach to the multi-sample test of means with imprecise data. Comput Stat Data Anal 51:148-162 · Zbl 1157.62391 · doi:10.1016/j.csda.2006.04.018
[75] Gil MA, Lubiano MA, de la Rosa de Sáa S, Sinova B (2015) Analyzing data from a fuzzy rating scale-based questionnaire. A case study. Psicothema 27(2):182-191
[76] Giordani P (2010) Three-way analysis of imprecise data. J Multivar Anal 101:568-582 · Zbl 1181.62100 · doi:10.1016/j.jmva.2009.10.003
[77] Giordani P, Kiers HAL (2006) A comparison of three methods for principal component analysis of fuzzy interval data. Comput Stat Data Anal 51:379-397 · Zbl 1157.62426 · doi:10.1016/j.csda.2006.02.019
[78] Gong MG, Su LZ, Jia M, Chen WS (2014) Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans Fuzzy Syst 22(1):98-109 · doi:10.1109/TFUZZ.2013.2249072
[79] González-Rodríguez G, Blanco-Fernández A, Colubi A, Lubiano MA (2009) Estimation of a simple linear regression model for fuzzy random variables. Fuzzy Sets Syst 160(3):357-370 · Zbl 1175.62073 · doi:10.1016/j.fss.2008.07.007
[80] González-Rodríguez G, Colubi A, Gil MA (2012) Fuzzy data treated as functional data: a one-way ANOVA test approach. Comput Stat Data Anal 56(4):943-955 · Zbl 1243.62104 · doi:10.1016/j.csda.2010.06.013
[81] Grzegorzewski P (1998) Statistical inference about the median from vague data. Control Cybern 27(3):447-464 · Zbl 0945.62038
[82] Grzegorzewski P, Hryniewicz O (2000) Soft methods in statistical quality control. Control Cybern 29(1):119-140 · Zbl 1030.90019
[83] Grzegorzewski P, Szymanowski H (2014) Goodness-of-fit tests for fuzzy data. Inform Sci 288(1):374-386 · Zbl 1357.62218 · doi:10.1016/j.ins.2014.08.008
[84] Hathaway RJ, Bezdek JC (2001) Fuzzy c-means clustering of incomplete data. IEEE Trans Syst Man Cybern Part B-Cybern 31:735-744 · doi:10.1109/3477.956035
[85] Hathaway RJ, Bezdek JC, Pedrycz W (1996) A parametric model for fusing heterogeneous fuzzy data. IEEE Trans Fuzzy Syst 4(3):270-281 · doi:10.1109/91.531770
[86] Havens TC, Bezdek JC, Leckie C, Hall LO, Palaniswami M (2012) Fuzzy c-means algorithms for very large data. IEEE Trans Fuzzy Syst 20:1130-1146 · doi:10.1109/TFUZZ.2012.2201485
[87] Hébert PA, Denoeux T, Masson MH (2006) Fuzzy multidimensional scaling. Comput Stat Data Anal 51:335-359 · Zbl 1157.62451 · doi:10.1016/j.csda.2006.02.020
[88] Hryniewicz O (2006) Possibilistic decisions and fuzzy statistical tests. Fuzzy Sets Syst 157(19):2665-2673 · Zbl 1099.62008 · doi:10.1016/j.fss.2003.08.009
[89] Huang Z, Ng MK (1999) A fuzzy k-modes algorithm for clustering categorical data. IEEE Trans Fuzzy Syst 7:446-452 · doi:10.1109/91.784206
[90] Hung W-L, Lee J-S, Fuh C-D (2004) Fuzzy clustering based on intuitionistic fuzzy relations. Int J Uncertain Fuzz Know-Based Syst 12:513-529 · Zbl 1097.68121 · doi:10.1142/S0218488504002953
[91] Hwang C, Rhee F (2007) Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. IEEE Trans Fuzzy Syst 15:107-120 · doi:10.1109/TFUZZ.2006.889763
[92] Irpino A, Verde R, de Carvalho FAT (2017) Fuzzy clustering of distributional data with automatic weighting of variable components. Inform Sci 406-407:248-268 · Zbl 1429.62249 · doi:10.1016/j.ins.2017.04.040
[93] Jain A, Dubes R (1988) Algorithms for clustering data. Prentice-Hall, Upper Saddle River · Zbl 0665.62061
[94] Kesemen O, Tezel Ö, Özkul E (2016) Fuzzy c-means clustering algorithm for directional data (FCM4DD). Exp Syst Appl 58:76-82 · doi:10.1016/j.eswa.2016.03.034
[95] Körner R (2000) An asymptotic \[\alpha\] α-test for the expectation of random fuzzy variables. J Stat Plan Inference 83(2):331-346 · Zbl 0976.62013 · doi:10.1016/S0378-3758(99)00107-X
[96] Krishnapuram R, Keller J (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1:98-110 · doi:10.1109/91.227387
[97] Kruse R (1984) Statistical estimation with linguistic data. Inform Sci 33(3):197-207 · Zbl 0588.62006 · doi:10.1016/0020-0255(84)90028-8
[98] Kruse R (1987) On a software tool for statistics with linguistic data. Fuzzy Sets Syst 24(3):377-383 · doi:10.1016/0165-0114(87)90034-0
[99] Kruse R, Meyer KD (1987) Statistics with vague data. Mathematical and statistical methods. Series theory and decision library B, vol 6. D. Reidel Pub Co., Dordrecht · Zbl 0663.62010
[100] Kruse, R.; Held, P.; Moewes, C.; Seising, R. (ed.); Trillas, E. (ed.); Moraga, C. (ed.); Termini, S. (ed.), On fuzzy data analysis, No. 298, 343-347 (2013), Heidelberg
[101] Kwakernaak H (1978) Fuzzy random variables, part I: definitions and theorems. Inform Sci 15:1-15 · Zbl 0438.60004 · doi:10.1016/0020-0255(78)90019-1
[102] Kwakernaak H (1979) Fuzzy random variables, part II: algorithms and examples for the discrete case. Inform Sci 17:253-278 · Zbl 0438.60005 · doi:10.1016/0020-0255(79)90020-3
[103] Laviolette M, Seaman JW, Barrett JD, Woodall WH (1995) A probabilistic and statistical view of fuzzy methods. Technometrics 37(3):249-261 · Zbl 0837.62081 · doi:10.1080/00401706.1995.10484327
[104] Lee M, Pedrycz W (2009) The fuzzy C-means algorithm with fuzzy P-mode prototypes for clustering objects having mixed features. Fuzzy Sets Syst 160:3590-3600 · Zbl 1185.68601 · doi:10.1016/j.fss.2009.06.015
[105] Lertworaprachaya Y, Yang Y, John R (2014) Interval-valued fuzzy decision trees with optimal neighbourhood perimeter. Appl Soft Comput 24:851-866 · doi:10.1016/j.asoc.2014.08.060
[106] Lingras P, West C (2004) Interval set clustering of web users with rough k-means. J Intel Inform Syst 23:5-16 · Zbl 1074.68586 · doi:10.1023/B:JIIS.0000029668.88665.1a
[107] Liu J (2010) Detecting the fuzzy clusters of complex networks. Pattern Recog 43:1334-1345 · Zbl 1192.68589 · doi:10.1016/j.patcog.2009.11.007
[108] Liu S, Matzavinos A, Sethuraman S (2013) Random walk distances in data clustering and applications. Adv Data Anal Classif 7(1):83-108 · Zbl 1261.62059 · doi:10.1007/s11634-013-0125-7
[109] Lubiano MA, Gil MA (1999) Estimating the expected value of fuzzy random variables in random samplings from finite populations. Stat Pap 40(3):277-295 · Zbl 0942.62010 · doi:10.1007/BF02929876
[110] Lubiano MA, de la Rosa de Sáa S, Montenegro M, Sinova M, Gil MA (2016a) Descriptive analysis of responses to items in questionnaires. Why not using a fuzzy rating scale? Inform Sci 360:131-148 · Zbl 1346.62027 · doi:10.1016/j.ins.2016.04.029
[111] Lubiano MA, Montenegro M, Sinova B, de la Rosa de Sáa S, Gil MA (2016b) Hypothesis testing for means in connection with fuzzy rating scale-based data: algorithms and applications. Eur J Oper Res 251(3):918-929 · Zbl 1346.62027 · doi:10.1016/j.ejor.2015.11.016
[112] Lubiano MA, Salas A, Carleos C, de la Rosa de Sáa S, Gil MA (2017) Hypothesis testing-based comparative analysis between rating scales for intrinsically imprecise data. Int J Approx Reason 88:128-147 · Zbl 1429.62112 · doi:10.1016/j.ijar.2017.05.007
[113] Maharaj EA, D’Urso P (2011) Fuzzy clustering of time series in the frequency domain. Inform Sci 181:1187-1211 · Zbl 1215.62061 · doi:10.1016/j.ins.2010.11.031
[114] Miyamoto S, Ichihashi H, Honda K (2008) Algorithms for fuzzy clustering—methods in c-means clustering with applications. Springer, Berlin · Zbl 1147.68073
[115] Montenegro M, Casals MR, Lubiano MA, Gil MA (2001) Two-sample hypothesis tests of means of a fuzzy random variable. Inform Sci 133(1-2):89-100 · Zbl 1042.62012 · doi:10.1016/S0020-0255(01)00078-0
[116] Näther W (1997) Linear statistical inference for random fuzzy data. Statistics 29(3):221-240 · Zbl 1030.62530 · doi:10.1080/02331889708802586
[117] Näther W (2006) Regression with random fuzzy data. Comput Stat Data Anal 51(1):235-252 · Zbl 1157.62463 · doi:10.1016/j.csda.2006.02.021
[118] Näther W, Albrecht M (1990) Linear regression with random fuzzy observations. Statistics 21(4):521-531 · Zbl 0714.62063 · doi:10.1080/02331889008802262
[119] Nguyen-Trang T, Vo-Van T (2017) A new approach for determining the prior probabilities in the classification problem by Bayesian method. Adv Data Anal Classif 11(3):629-643 · Zbl 1414.62261 · doi:10.1007/s11634-016-0253-y
[120] Okuda T, Tanaka H, Asai K (1978) A formulation of fuzzy decision problems with fuzzy information using probability measures of fuzzy events. Inform Control 38:135-147 · Zbl 0401.94050 · doi:10.1016/S0019-9958(78)90151-1
[121] Parchami A, Taheri SM, Mashinchi M (2009) Fuzzy \[p\] p-value in testing fuzzy hypotheses with crisp data. Stat Pap 51(1):209-226 · Zbl 1247.62105 · doi:10.1007/s00362-008-0133-4
[122] Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybern Part B-Cybern 28:103-109 · doi:10.1109/3477.658584
[123] Pedrycz W, Bezdek JC, Hathaway RJ, Rogers GW (1998) Two nonparametric models for fusing heterogeneous fuzzy data. IEEE Trans Fuzzy Syst 6(3):411-425 · doi:10.1109/91.705509
[124] Pham DL (2001) Spatial models for fuzzy clustering. Comput Vis Image Underst 84:285-297 · Zbl 1033.68612 · doi:10.1006/cviu.2001.0951
[125] Puri ML, Ralescu DA (1985) The concept of normality for fuzzy random variables. Ann Probab 11:1373-1379 · Zbl 0583.60011 · doi:10.1214/aop/1176992822
[126] Puri ML, Ralescu DA (1986) Fuzzy random variables. J Math Anal Appl 114:409-422 · Zbl 0592.60004 · doi:10.1016/0022-247X(86)90093-4
[127] Ramos-Guajardo AB, Lubiano MA (2012) K-sample tests for equality of variances of random fuzzy sets. Comput Stat Data Anal 56:956-966 · Zbl 1243.62024 · doi:10.1016/j.csda.2010.11.025
[128] Ramos-Guajardo AB, Colubi A, González-Rodríguez G, Gil MA (2010) One-sample tests for a generalized Fréchet variance of a fuzzy random variable. Metrika 71:185-202 · Zbl 1182.62103 · doi:10.1007/s00184-008-0225-0
[129] Rocci R, Vichi M (2005) Three-mode component analysis with crisp or fuzzy partition of units. Psychometrika 70(4):715-736 · Zbl 1306.62491 · doi:10.1007/s11336-001-0926-z
[130] Ross TJ, Booker JM, Parkinson WJ (eds) (2002) Fuzzy logic and probability applications: bridging the gap. ASA-SIAM series on statistics and applied probability. SIAM, Philadelphia · Zbl 1005.00012
[131] Ruan JH, Wang XP, Chan FTS, Shi Y (2016) Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. Int J Prod Res 54(14):4368-4386 · doi:10.1080/00207543.2016.1174344
[132] Runkler TA, Bezdek JC (2003) Web mining with relational clustering. Int J Approx Reason 32:217-236 · Zbl 1026.68006 · doi:10.1016/S0888-613X(02)00084-1
[133] Ruspini EH (1969) A new approach to clustering. Inform Control 15:22-32 · Zbl 0192.57101 · doi:10.1016/S0019-9958(69)90591-9
[134] Ruspini EH (1970) Numerical methods for fuzzy clustering. Inform Sci 2:319-350 · Zbl 0205.21301 · doi:10.1016/S0020-0255(70)80056-1
[135] Shan J, Cheng HD, Wang Y (2012) A novel segmentation method for breast ultrasound images based on neutrosophic l-means clustering. Med Phys 3:5669-5682 · doi:10.1118/1.4747271
[136] Silva L, Moura E, Canuto AMP, Santiago RHN, Bedregal B (2015) An interval-based framework for fuzzy clustering applications. IEEE Trans Fuzzy Syst 23:2174-2186 · doi:10.1109/TFUZZ.2015.2407901
[137] Singpurwalla ND, Booker JM (2004) Membership functions and probability measures of fuzzy sets. J Am Stat Assoc 99(467):867-877 · Zbl 1117.62425 · doi:10.1198/016214504000001196
[138] Sinova B, Gil MA, Van Aelst S (2016) M-estimates of location for the robust central tendency of fuzzy data. IEEE Trans Fuzzy Syst 24(4):945-956 · doi:10.1109/TFUZZ.2015.2489245
[139] Skala HJ (1975) Non-Archimedean utility theory. Series theory and decision library, vol 9. D. Reidel Pub Co., Dordrecht · Zbl 0308.02058 · doi:10.1007/978-94-010-1724-4
[140] Son LH (2015) DPFCM: a novel distributed picture fuzzy clustering method on picture fuzzy sets. Exp Syst Appl 42:51-66 · doi:10.1016/j.eswa.2014.07.026
[141] Statistical science issue on artificial intelligence and expert systems. Stat Sci 2(1):3-44
[142] Tamura S, Higuchi S, Tanaka K (1971) Pattern classification based on fuzzy relations. IEEE Trans Syst Man Cybern 1:61-66 · Zbl 0224.68012 · doi:10.1109/TSMC.1971.5408605
[143] Tan T, Suk HW, Hwang H, Lim J (2013) Functional fuzzy clusterwise regression analysis. Adv Data Anal Classif 7(1):57-82 · Zbl 1271.62138 · doi:10.1007/s11634-013-0126-6
[144] Tanaka H, Watada J (1988) Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst 27(3):275-289 · Zbl 0662.93066 · doi:10.1016/0165-0114(88)90054-1
[145] Tanaka H, Uejima S, Asai K (1982) Linear regression analysis with fuzzy model. IEEE Trans Syst Man Cybern 12(6):903-907 · Zbl 0501.90060 · doi:10.1109/TSMC.1982.4308925
[146] Theodorou Y, Drossos C, Alevizos P (2007) Correspondence analysis with fuzzy data: the fuzzy eigenvalue problem. Fuzzy Sets Syst 158:704-721 · Zbl 1110.62083 · doi:10.1016/j.fss.2006.11.011
[147] Tokushige S, Yadohisa H, Inada K (2007) Crisp and fuzzy k-means clustering algorithms for multivariate functional data. Comput Stat 22:1-16 · Zbl 1196.62089 · doi:10.1007/s00180-006-0013-0
[148] Viertl R (2006) Univariate statistical analysis with fuzzy data. Comput Stat Data Anal 51(1):133-147 · Zbl 1157.62368 · doi:10.1016/j.csda.2006.04.002
[149] Wang D (2004) A note on consistency and unbiasedness of point estimation with fuzzy data. Metrika 60:93-104 · Zbl 1049.62027 · doi:10.1007/s001840300298
[150] Watanabe N, Imaizumi T (1993) A fuzzy statistical test of fuzzy hypotheses. Fuzzy Sets Syst 53:167-178 · Zbl 0795.62025 · doi:10.1016/0165-0114(93)90170-M
[151] Wu H-C (2005) Statistical hypotheses testing for fuzzy data. Inform Sci 279:446-459
[152] Wu K-L, Yang M-S (2002) Alternative c-means clustering algorithms. Pattern Recogn 35(10):2267-2278 · Zbl 1006.68876 · doi:10.1016/S0031-3203(01)00197-2
[153] Yamashita N, Mayekawa S-I (2015) A new biplot procedure with joint classification of objects and variables by fuzzy c-means clustering. Adv Data Anal Classif 9(3):243-266 · Zbl 1414.62022 · doi:10.1007/s11634-014-0184-4
[154] Yang M-S, Nataliani Y (2017) Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recogn 71:45-59 · doi:10.1016/j.patcog.2017.05.017
[155] Yang M-S, Pan J-A (1997) On fuzzy clustering of directional data. Fuzzy Sets Syst 91:319-326 · Zbl 0921.62077 · doi:10.1016/S0165-0114(96)00157-1
[156] Yang M-S, Hwang P-Y, Chen D-H (2004) Fuzzy clustering algorithms for mixed feature variables. Fuzzy Sets Syst 141:301-317 · Zbl 1137.62350 · doi:10.1016/S0165-0114(03)00072-1
[157] Zadeh LA (1965) Fuzzy sets. Inform Control 8(3):338-353 · Zbl 0139.24606 · doi:10.1016/S0019-9958(65)90241-X
[158] Zadeh LA (1968) Probability measures of fuzzy events. J Math Anal Appl 23:421-427 · Zbl 0174.49002 · doi:10.1016/0022-247X(68)90078-4
[159] Zadeh LA (1975a) The concept of a linguistic variable and its application to approximate reasoning. Part 1. Inform Sci 8:199-249 · Zbl 0397.68071 · doi:10.1016/0020-0255(75)90036-5
[160] Zadeh LA (1975b) The concept of a linguistic variable and its application to approximate reasoning. Part 2. Inform Sci 8:301-353 · Zbl 0404.68074 · doi:10.1016/0020-0255(75)90046-8
[161] Zadeh LA (1975c) The concept of a linguistic variable and its application to approximate reasoning. Part 3. Inform Sci 9:43-80 · Zbl 0404.68075 · doi:10.1016/0020-0255(75)90017-1
[162] Zadeh LA (1995) Discussion: probability theory and fuzzy logic are complementary rather than competitive. Technometrics 37(3):271-276 · doi:10.1080/00401706.1995.10484330
[163] Zadeh LA (2004) Comment: membership functions and probability measures of fuzzy sets. J Am Stat Assoc 99(467):880-881 · doi:10.1198/016214504000001222
[164] Zadeh LA (2015) Fuzzy logic—a personal perspective. Fuzzy Sets Syst 281:4-20 · Zbl 1368.03037 · doi:10.1016/j.fss.2015.05.009
[165] Zhou J, Hung CC, Wang X, Chen S (2007) Fuzzy clustering based on credibility measure. In: Proceedings of the 6th international conference on management science, Lhasa, pp 404-411
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