Mixture Densities, Maximum Likelihood and the EM Algorithm, SIAM Review, vol.26, issue.2, pp.195-239, 1984. ,
DOI : 10.1137/1026034
Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society B, pp.39-40, 1977. ,
CEM algorithm for imprecise data. application to flaw diagnosis using acoustic emission The Hague, Proceedings of the IEEE International conference on Systems, pp.4774-4779, 2004. ,
URL : https://hal.archives-ouvertes.fr/hal-01377399
DISEASE CLUSTER STATISTICS FOR IMPRECISE SPACE-TIME LOCATIONS, Statistics in Medicine, vol.15, issue.7-9, pp.873-885, 1996. ,
DOI : 10.1002/(SICI)1097-0258(19960415)15:7/9<873::AID-SIM256>3.0.CO;2-U
Expression of Measurement Uncertainty in a Very Limited Knowledge Context: A Possibility Theory-Based Approach, IEEE Transactions on Instrumentation and Measurement, vol.56, issue.3, pp.731-735, 2007. ,
DOI : 10.1109/TIM.2007.894918
URL : https://hal.archives-ouvertes.fr/hal-00428586
Fuzzy set-theoretic methods in statistics of Handbook of Fuzzy Sets, pp.311-347, 1998. ,
Overview on the development of fuzzy random variables, Fuzzy Sets and Systems, pp.2546-2557, 2006. ,
Univariate statistical analysis with fuzzy data, Computational Statistics & Data Analysis, vol.51, issue.1, pp.133-147, 2006. ,
DOI : 10.1016/j.csda.2006.04.002
Ontic vs. epistemic fuzzy sets in modeling and data processing tasks, Proceedings of the 2011 International Conference on Neural Computation: Theory and Applications (NCTA'2011), 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-01153809
Statistical reasoning with set-valued information: Ontic vs. epistemic views, International Journal of Approximate Reasoning In Press ,
URL : https://hal.archives-ouvertes.fr/hal-01153809
An approach of clustering data with noisy or imprecise feature measurement, Pattern Recognition Letters, vol.19, issue.14, pp.1307-1313, 1998. ,
DOI : 10.1016/S0167-8655(98)00112-3
Clustering reduced interval data using Hausdorff distance, Computational Statistics, vol.15, issue.1, pp.241-288, 2006. ,
DOI : 10.1007/s00180-006-0263-x
Error-aware densitybased clustering of imprecise measurement values, Proceedings of the 7th International Conference on Data Mining Workshops (ICDM'07), pp.471-476, 2007. ,
Mixture Model Clustering of Uncertain Data, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., pp.879-884, 2005. ,
DOI : 10.1109/FUZZY.2005.1452510
URL : https://hal.archives-ouvertes.fr/hal-00736370
EM algorithms for multivariate Gaussian mixture models with truncated and censored data, Computational Statistics & Data Analysis, vol.56, issue.9, pp.2816-2829, 2012. ,
DOI : 10.1016/j.csda.2012.03.003
Hierarchical Density-Based Clustering of Uncertain Data, Fifth IEEE International Conference on Data Mining (ICDM'05), pp.689-692, 2005. ,
DOI : 10.1109/ICDM.2005.75
Density-based clustering of uncertain data, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , KDD '05, pp.672-677, 2005. ,
DOI : 10.1145/1081870.1081955
Uncertain Data Mining: An Example in Clustering Location Data, Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining LNAI-3918 of Lecture Notes in Artificial Intelligence, pp.199-204, 2006. ,
DOI : 10.1007/11731139_24
Efficient Clustering of Uncertain Data, Sixth International Conference on Data Mining (ICDM'06), pp.436-445, 2006. ,
DOI : 10.1109/ICDM.2006.63
Clustering uncertain data using clustering uncertain data using Voronoi diagrams and Rtree index, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.9, pp.1219-1233, 2010. ,
Approximation algorithms for clustering uncertain data, Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '08, pp.191-200, 2008. ,
DOI : 10.1145/1376916.1376944
On Density Based Transforms for Uncertain Data Mining, 2007 IEEE 23rd International Conference on Data Engineering, pp.866-875, 2007. ,
DOI : 10.1109/ICDE.2007.367932
Subspace Clustering for Uncertain Data, Proceedings of the SIAM International Conference on Data Mining, pp.385-396, 2010. ,
DOI : 10.1137/1.9781611972801.34
Managing Uncertain Data: Probabilistic Approaches, 2008 The Ninth International Conference on Web-Age Information Management, pp.405-412, 2008. ,
DOI : 10.1109/WAIM.2008.42
Managing and Mining Uncertain Data, no. 35 in Advances in Database Systems, 2009. ,
A Survey of Uncertain Data Algorithms and Applications, IEEE Transactions on Knowledge and Data Engineering, vol.21, issue.5, pp.609-623, 2009. ,
DOI : 10.1109/TKDE.2008.190
Fuzzy clustering model for fuzzy data, Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, pp.2123-2128, 1995. ,
DOI : 10.1109/FUZZY.1995.409973
A parametric model for fusing heterogeneous fuzzy data, IEEE Transactions on Fuzzy Systems, vol.4, issue.3, pp.1277-1282, 1996. ,
DOI : 10.1109/91.531770
Two nonparametric models for fusing heterogeneous fuzzy data, IEEE Transactions on Fuzzy Systems, vol.6, issue.3, pp.411-425, 1998. ,
DOI : 10.1109/91.705509
Clustering of data with uncertainties using hausdorff distance, Proceedings of the IEEE International conference on Intelligence Processing Systems, pp.67-71, 1998. ,
Fuzzy clustering of data with uncertainties using minimum and maximum distances based on l1 metric, Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp.2511-2516, 2001. ,
Fuzzy clustering procedures for conical fuzzy vector data, Fuzzy Sets and Systems, pp.189-200, 1999. ,
Fuzzy clustering algorithms for mixed feature variables, Fuzzy Sets and Systems, pp.301-317, 2004. ,
Three-way fuzzy clustering models for LR fuzzy time trajectories, Computational Statistics & Data Analysis, vol.43, issue.2, pp.149-177, 2003. ,
DOI : 10.1016/S0167-9473(02)00226-8
A weighted fuzzy c-means clustering model for fuzzy data, Computational Statistics and Data Analysis, vol.50, issue.6, pp.1496-1523, 2006. ,
Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation, Fuzzy Sets and Systems, vol.150, issue.3, pp.561-577, 2005. ,
DOI : 10.1016/j.fss.2004.04.007
A Fuzzy Clustering Model for Fuzzy Data with Outliers, International Journal of Fuzzy System Applications, vol.1, issue.2, pp.29-42, 2011. ,
DOI : 10.4018/ijfsa.2011040103
Fuzzy and possibilistic clustering for fuzzy data, Computational Statistics & Data Analysis, vol.56, issue.4, pp.915-927, 2012. ,
DOI : 10.1016/j.csda.2010.09.013
Clustering approach using belief function theory, Artificial Intelligence: Methodology, Systems, and Applications LNAI-4183 of Lecture Notes in Artificial Intelligence, pp.162-171, 2006. ,
Clustering Fuzzy Data Using the Fuzzy??EM??Algorithm, Proceedings of the 4th International Conference on Scalable Uncertainty Management (SUM'2010) LNAI- 6379 of Lecture Notes in Artificial Intelligence, pp.333-346, 2010. ,
DOI : 10.1007/978-3-642-15951-0_31
URL : https://hal.archives-ouvertes.fr/hal-00491428
Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified Observations, Journal of the American Statistical Association, vol.70, issue.2, pp.403-406, 1977. ,
DOI : 10.1080/01621459.1977.10481009
A k-nearest neighbor classification rule based on Dempster-Shafer theory, IEEE Transactions on Systems, Man, and Cybernetics, vol.25, issue.5, pp.804-813, 1995. ,
DOI : 10.1109/21.376493
EM Algorithm for Partially Known Labels, Proceedings of the 7th Conference of the International Federation of Classification Societies, pp.161-166, 2000. ,
DOI : 10.1007/978-3-642-59789-3_26
Learning from an imprecise teacher: probabilistic and evidential approaches, Proceedings of ASMDA'01, pp.100-105, 2001. ,
Logistic regression for partial labels, 9th International Conference on Information Processing and Management of Uncertainty in Knowlege-based Systems (IPMU'02), pp.1935-1941, 2002. ,
Learning from ambiguously labeled examples, Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA 05), pp.168-179, 2005. ,
Estimating a kernel fisher discriminant in the presence of label noise, Proceedings of the 18th International Conference on Machine Learning, pp.306-313, 2001. ,
Semi-supervised learning with an imperfect supervisor, Knowledge and Information Systems, vol.22, issue.1, pp.385-413, 2005. ,
DOI : 10.1007/s10115-005-0219-4
URL : https://hal.archives-ouvertes.fr/hal-01170739
A boosting approach to remove class label noise, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), pp.6-9, 2005. ,
DOI : 10.1109/ICHIS.2005.1
Partially Supervised Learning by a Credal EM Approach, Proceedings of the Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.3571-956, 2005. ,
DOI : 10.1007/11518655_80
Estimation de modèles de mélanges finis par un algorithm em crédibiliste, Traitement du Signal, vol.24, issue.2, pp.103-113, 2007. ,
Belief Classification Approach Based on Generalized Credal EM, Proceedings of the Ninth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.4724-524, 2005. ,
DOI : 10.1007/11518655_80
Learning from partially supervised data using mixture models and belief functions, Pattern Recognition, vol.42, issue.3, pp.334-348, 2009. ,
DOI : 10.1016/j.patcog.2008.07.014
Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation, IEEE Transactions on Image Processing, vol.6, issue.3, pp.425-440, 1997. ,
DOI : 10.1109/83.557353
Fuzzy Gaussian Mixture Models, Pattern Recognition, vol.45, issue.3, pp.1146-1158, 2012. ,
DOI : 10.1016/j.patcog.2011.08.028
Belief Hierarchical Clustering, Belief Functions: Theory and Applications LNAI-8764 of Lecture Notes in Artificial Intelligence, pp.68-76, 2014. ,
DOI : 10.1109/5326.669565
URL : https://hal.archives-ouvertes.fr/hal-01102028
Type-2 fuzzy Gaussian mixture models, Pattern Recognition, vol.41, issue.12, pp.3636-3643, 2008. ,
DOI : 10.1016/j.patcog.2008.06.006
Gaussian parsimonious clustering models, Pattern Recognition, vol.28, issue.5, pp.781-793, 1995. ,
DOI : 10.1016/0031-3203(94)00125-6
URL : https://hal.archives-ouvertes.fr/inria-00074643
Fuzzy sets, Information and Control, vol.8, issue.3, pp.338-353, 1965. ,
DOI : 10.1016/S0019-9958(65)90241-X
Maximum likelihood estimation from fuzzy data using the EM algorithm, Fuzzy Sets and Systems, vol.183, issue.1, pp.72-91, 2011. ,
DOI : 10.1016/j.fss.2011.05.022
URL : https://hal.archives-ouvertes.fr/hal-00654118
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.1, pp.119-130, 2013. ,
DOI : 10.1109/TKDE.2011.201
URL : https://hal.archives-ouvertes.fr/hal-00804343
Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization, International Journal of Approximate Reasoning In Press ,
Probability measures of Fuzzy events, Journal of Mathematical Analysis and Applications, vol.23, issue.2, pp.421-427, 1968. ,
DOI : 10.1016/0022-247X(68)90078-4
Fast simulation of truncated Gaussian distributions, Statistics and Computing, vol.82, issue.398, pp.275-288, 2011. ,
DOI : 10.1007/s11222-009-9168-1
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering, Journal of Classification, vol.24, issue.2, pp.155-181, 2007. ,
DOI : 10.1007/s00357-007-0004-5
Bayesian regularization for normal mixture estimation and model-based clustering, Tech. rep, 2009. ,
Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985. ,
DOI : 10.1007/BF01908075
Trapezoidal and triangular distributions for Type B evaluation of standard uncertainty, Metrologia, vol.44, issue.2, pp.117-127, 2007. ,
DOI : 10.1088/0026-1394/44/2/003