Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A procedure for the detection of multivariate outliers
Computational Statistics & Data Analysis
Fuzzy sets and their application to clustering and training
Fuzzy sets and their application to clustering and training
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Robust mixture modelling using the t distribution
Statistics and Computing
Collaborative fuzzy clustering
Pattern Recognition Letters
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Multivariate Student-t self-organizing maps
Neural Networks
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Robust fuzzy clustering using adaptive fuzzy meridians
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Expert Systems with Applications: An International Journal
Hybrid fuzzy clustering using LP norms
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Robust data clustering by learning multi-metric Lq-norm distances
Expert Systems with Applications: An International Journal
A possibilistic clustering approach toward generative mixture models
Pattern Recognition
Model-based clustering via linear cluster-weighted models
Computational Statistics & Data Analysis
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In this paper, we propose a robust fuzzy clustering algorithm, based on a fuzzy treatment of finite mixtures of multivariate Student's-t distributions, using the fuzzy c-means (FCM) algorithm. As we experimentally demonstrate, the proposed algorithm, by incorporating the assumptions about the probabilistic nature of the clusters being dirived into the fuzzy clustering procedure, allows for the exploitation of the hard tails of the multivariate Student's-t distribution, to obtain a robust to outliers fuzzy clustering algorithm, offering increased clustering performance comparing to existing FCM-based algorithms. Our experimental results prove that the proposed fuzzy treatment of finite mixtures of Student's-t distributions is more effective comparing to their statistical treatments using EM-type algorithms, while imposing comparable computational loads.