Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deterministic annealing EM algorithm
Neural Networks
Mixtures of probabilistic principal component analyzers
Neural Computation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
From a Gaussian mixture model to additive fuzzy systems
IEEE Transactions on Fuzzy Systems
Regularized Linear Fuzzy Clustering and Probabilistic PCA Mixture Models
IEEE Transactions on Fuzzy Systems
Pattern Recognition
A possibilistic clustering approach toward generative mixture models
Pattern Recognition
Parallel tempering MCMC acceleration using reconfigurable hardware
ARC'12 Proceedings of the 8th international conference on Reconfigurable Computing: architectures, tools and applications
Fuzzy semi-supervised co-clustering for text documents
Fuzzy Sets and Systems
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In this paper, we establish a novel regard towards fuzzy clustering, showing it provides a sound framework for fitting finite mixture models. We propose a novel fuzzy clustering-type methodology for finite mixture model fitting, effected by utilizing a regularized form of the fuzzy c-means (FCM) algorithm, and introducing a proper dissimilarity functional for the algorithm with respect to the probabilistic properties of the model being treated. We apply the proposed methodology in a number of popular finite mixture models, and the corresponding expressions of the fuzzy model fitting algorithm are derived. We examine the efficacy of our novel approach in both clustering and classification applications of benchmark data sets, and we demonstrate the advantages of the proposed approach over maximum-likelihood.