Clustering and classification of fuzzy data using the fuzzy EM algorithm

DOI10.1016/j.fss.2015.04.012OpenAlexW1999176848MaRDI QIDQ1697327

Thierry Denoeux, Benjamin Quost

Publication date: 19 February 2018

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2015.04.012

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Multivariate analysis and fuzziness (62H86)

Related Items (10)

Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model ⋮ Bayesian and frequentist approaches on estimation and testing for a zero-inflated binomial distribution ⋮ Possibilistic and fuzzy clustering methods for robust analysis of non-precise data ⋮ Feature selection and disambiguation in learning from fuzzy labels using rough sets ⋮ Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression ⋮ A fuzzy clustering approach for fuzzy data based on a generalized distance ⋮ Likelihood-fuzzy analysis: from data, through statistics, to interpretable fuzzy classifiers ⋮ A general framework for maximizing likelihood under incomplete data ⋮ Clustering of fuzzy data and simultaneous feature selection: a model selection approach ⋮ Parameter estimation from interval-valued data using the expectation-maximization algorithm

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Maximum likelihood estimation from fuzzy data using the EM algorithm
• Fuzzy and possibilistic clustering for fuzzy data
• Fuzzy Gaussian mixture models
• Fast simulation of truncated Gaussian distributions
• Overview on the development of fuzzy random variables
• Clustering reduced interval data using Hausdorff distance
• Type-2 fuzzy Gaussian mixture models
• Managing and mining uncertain data
• Three-way fuzzy clustering models for LR fuzzy time trajectories
• A weighted fuzzy \(C\)-means clustering model for fuzzy data
• Learning from partially supervised data using mixture models and belief functions
• Univariate statistical analysis with fuzzy data
• Bayesian regularization for normal mixture estimation and model-based clustering
• Statistics with vague data
• Fuzzy clustering procedures for conical fuzzy vector data
• Fuzzy clustering algorithms for mixed feature variables.
• Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation
• EM algorithms for multivariate Gaussian mixture models with truncated and censored data
• Statistical reasoning with set-valued information: ontic vs. epistemic views
• Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization
• Probability measures of fuzzy events
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• Belief Classification Approach Based on Generalized Credal EM
• Clustering Approach Using Belief Function Theory
• Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified Observations
• An approach of clustering data with noisy or imprecise feature measurement
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty
• Fuzzy sets
• Advances in Intelligent Data Analysis VI

This page was built for publication: Clustering and classification of fuzzy data using the fuzzy EM algorithm