Multivariate statistics: a practical approach
Multivariate statistics: a practical approach
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering algorithms based on the maximum likelihood principle
Fuzzy Sets and Systems
Characterization and detection of noise in clustering
Pattern Recognition Letters
On a class of fuzzy classification maximum likelihood procedures
Fuzzy Sets and Systems
Fuzzy clustering with high contrast
Journal of Computational and Applied Mathematics - Special issue in honor of Professor Dr. F. Broeckx
Application of the least trimmed squares technique to prototype-based clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Fuzzy clustering using scatter matrices
Computational Statistics & Data Analysis - Special issue on classification
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Towards a robust fuzzy clustering
Fuzzy Sets and Systems - Data analysis
A Novel Approach to Noise Clustering for Outlier Detection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
Unsupervised possibilistic clustering
Pattern Recognition
Exploring the number of groups in robust model-based clustering
Statistics and Computing
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Switching regression models and fuzzy clustering
IEEE Transactions on Fuzzy Systems
A fast algorithm for robust constrained clustering
Computational Statistics & Data Analysis
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It is well-known that outliers and noisy data can be very harmful when applying clustering methods. Several fuzzy clustering methods which are able to handle the presence of noise have been proposed. In this work, we propose a robust clustering approach called F-TCLUST based on trimming a fixed proportion of observations that are (''impartially'') determined by the data set itself. The proposed approach also considers an eigenvalue ratio constraint that makes it a mathematically well-defined problem and serves to control the allowed differences among cluster scatters. A computationally feasible algorithm is proposed for its practical implementation. Some guidelines about how to choose the parameters controlling the performance of the fuzzy clustering procedure are also given.