Characterization and detection of noise in clustering
Pattern Recognition Letters
On a class of fuzzy c-numbers clustering procedures for fuzzy data
Fuzzy Sets and Systems
Fuzzy clustering procedures for conical fuzzy vector data
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Unsupervised possibilistic clustering
Pattern Recognition
Possibilistic fuzzy co-clustering of large document collections
Pattern Recognition
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
Fuzzy and possibilistic clustering for fuzzy data
Computational Statistics & Data Analysis
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
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
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
A Robust Automatic Merging Possibilistic Clustering Method
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Efficient stochastic algorithms for document clustering
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Optimal clustering in the context of overlapping cluster analysis
Information Sciences: an International Journal
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In possibilistic clustering objects are assigned to clusters according to the so-called membership degrees taking values in the unit interval. Differently from fuzzy clustering, it is not required that the sum of the membership degrees of an object to all clusters is equal to one. This is very helpful in the presence of outliers, which are usually assigned to the clusters with membership degrees close to zero. Unfortunately, a drawback of the possibilistic approach is the tendency to produce coincident clusters. A remedy is to add a repulsion term among prototypes in the loss function forcing the prototypes to be far 'enough' from each other. Here, a possibilistic clustering algorithm with repulsion constraints for imprecise data, managed in terms of fuzzy sets, is introduced. Applications to synthetic and real fuzzy data are considered in order to analyze how the proposed clustering algorithm works in practice.