Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
An introduction to variational methods for graphical models
Learning in graphical models
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
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Robust fuzzy clustering using mixtures of Student's-t distributions
Pattern Recognition Letters
Factor analysis latent subspace modeling and robust fuzzy clustering using t-distributions
IEEE Transactions on Fuzzy Systems
Dual fuzzy-possibilistic coclustering for categorization of documents
IEEE Transactions on Fuzzy Systems
A method for training finite mixture models under a fuzzy clustering principle
Fuzzy Sets and Systems
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
From a Gaussian mixture model to additive fuzzy systems
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Soft transition from probabilistic to possibilistic fuzzy clustering
IEEE Transactions on Fuzzy Systems
A Novel Similarity-Based Fuzzy Clustering Algorithm by Integrating PCM and Mountain Method
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.01 |
Generative mixture models (MMs) provide one of the most popular methodologies for unsupervised data clustering. MMs are formulated on the basis of the assumption that each observation derives from (belongs to) a single cluster. However, in many applications, data may intuitively belong to multiple classes, thus rendering the single-cluster assignment assumptions of MMs irrelevant. Furthermore, even in applications where a single-cluster data assignment is required, the induced multinomial allocation of the modeled data points to the clusters derived by a MM, imposing the constraint that the membership probabilities of a data point across clusters sum to one, makes MMs very vulnerable to the presence of outliers in the clustered data sets, and renders them ineffective in discriminating between cases of equal evidence or ignorance. To resolve these issues, in this paper we introduce a possibilistic formulation of MMs. Possibilistic clustering is a methodology that yields possibilistic data partitions, with the obtained membership values being interpreted as degrees of possibility (compatibilities) of the data points with respect to the various clusters. We provide an efficient maximum-likelihood fitting algorithm for the proposed model, and we conduct an objective evaluation of its efficacy using benchmark data.