Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
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
A Novel Approach to Noise Clustering for Outlier Detection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
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
Information Sciences: an International Journal
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
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
Analysis and efficient implementation of a linguistic fuzzy c-means
IEEE Transactions on Fuzzy Systems
Editorial: Special issue on fuzzy sets in statistics
Computational Statistics & Data Analysis
Generalized Bayesian inference in a fuzzy context: From theory to a virtual reality application
Computational Statistics & Data Analysis
On possibilistic clustering with repulsion constraints for imprecise data
Information Sciences: an International Journal
Self-Organizing Maps for imprecise data
Fuzzy Sets and Systems
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The Fuzzy k-Means clustering model (FkM) is a powerful tool for classifying objects into a set of k homogeneous clusters by means of the membership degrees of an object in a cluster. In FkM, for each object, the sum of the membership degrees in the clusters must be equal to one. Such a constraint may cause meaningless results, especially when noise is present. To avoid this drawback, it is possible to relax the constraint, leading to the so-called Possibilistic k-Means clustering model (PkM). In particular, attention is paid to the case in which the empirical information is affected by imprecision or vagueness. This is handled by means of LR fuzzy numbers. An FkM model for LR fuzzy data is firstly developed and a PkM model for the same type of data is then proposed. The results of a simulation experiment and of two applications to real world fuzzy data confirm the validity of both models, while providing indications as to some advantages connected with the use of the possibilistic approach.