Hierarchical mixtures of experts and the EM algorithm
Neural Computation
On a class of fuzzy c-numbers clustering procedures for fuzzy data
Fuzzy Sets and Systems
Fuzzy set-theoretic methods in statistics
Fuzzy sets in decision analysis, operations research and statistics
Fuzzy clustering procedures for conical fuzzy vector data
Fuzzy Sets and Systems
Three-way fuzzy clustering models for LR fuzzy time trajectories
Computational Statistics & Data Analysis
Fuzzy clustering of intuitionistic fuzzy data
International Journal of Business Intelligence and Data Mining
Clustering algorithm for intuitionistic fuzzy sets
Information Sciences: an International Journal
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
Univariate statistical analysis with fuzzy data
Computational Statistics & Data Analysis
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Analysis and efficient implementation of a linguistic fuzzy c-means
IEEE Transactions on Fuzzy Systems
Maximum likelihood estimation from fuzzy data using the EM algorithm
Fuzzy Sets and Systems
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In this article, we address the problem of clustering imprecise data using finite mixtures of Gaussians. We propose to estimate the parameters of the mixture model using the fuzzy EM algorithm. This extension of the EM algorithm allows us to handle imprecise data represented by fuzzy numbers. First, we briefly recall the principle of the fuzzy EM algorithm. Then, we provide the update equations for the parameters of a Gaussian mixture model for fuzzy data. Experiments carried out on synthetic and real data demonstrate the interest of our approach for clustering data that are only imprecisely known.