Algorithms for clustering data
Algorithms for clustering data
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
A multiclass neural network classifier with fuzzy teaching inputs
Fuzzy Sets and Systems
Fuzzy set-theoretic methods in statistics
Fuzzy sets in decision analysis, operations research and statistics
Fuzzy sets in decision analysis, operations research and statistics
Multidimensional scaling of interval-valued dissimilarity data
Pattern Recognition Letters
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Multidimensional scaling of fuzzy dissimilarity data
Fuzzy Sets and Systems - Clustering and modeling
Linguistic neurocomputing: the design of neural networks in the framework of fuzzy sets
Fuzzy Sets and Systems - Clustering and modeling
EVCLUS: evidential clustering of proximity data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recovery of the metric structure of a pattern of points using minimal information
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy clustering for symbolic data
IEEE Transactions on Fuzzy Systems
Analysis and efficient implementation of a linguistic fuzzy c-means
IEEE Transactions on Fuzzy Systems
Detecting outliers in interval data
Proceedings of the 44th annual Southeast regional conference
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Treatment of L-Fuzzy contexts with absent values
Information Sciences: an International Journal
International Journal of Approximate Reasoning
RECM: Relational evidential c-means algorithm
Pattern Recognition Letters
Belief Functions and Cluster Ensembles
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework
Pattern Recognition Letters
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
A new belief-based K-nearest neighbor classification method
Pattern Recognition
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
A belief classification rule for imprecise data
Applied Intelligence
| Hi-index | 0.10 |
The problem of clustering objects based on interval-valued dissimilarities is tackled in the framework of the Dempster--Shafer theory of belief functions. The proposed method assigns to each object a basic belief assignment (or mass function) defined on the set of clusters, in such a way that the belief and the plausibility that any two objects belong to the same cluster reflect, respectively, the observed lower and upper dissimilarity values. Experiments with synthetic and real data sets demonstrate the ability of the method to detect meaningful clusters, even in the presence of imprecise data and outliers.