A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
An adaptive ant-based clustering algorithm with improved environment perception
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Fuzzy PCA-guided robust k-means clustering
IEEE Transactions on Fuzzy Systems
Which brainstem cells generate the respiration cycles?
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
Tuning graded possibilistic clustering by visual stability analysis
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
A possibilistic clustering approach toward generative mixture models
Pattern Recognition
Several formulations for graded possibilistic approach to fuzzy clustering
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure
International Journal of Fuzzy System Applications
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model
International Journal of Approximate Reasoning
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
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In the fuzzy clustering literature, two main types of membership are usually considered: A relative type, termed probabilistic, and an absolute or possibilistic type, indicating the strength of the attribution to any cluster independent from the rest. There are works addressing the unification of the two schemes. Here, we focus on providing a model for the transition from one schema to the other, to exploit the dual information given by the two schemes, and to add flexibility for the interpretation of results. We apply an uncertainty model based on interval values to memberships in the clustering framework, obtaining a framework that we term graded possibility. We outline a basic example of graded possibilistic clustering algorithm and add some practical remarks about its implementation. The experimental demonstrations presented highlight the different properties attainable through appropriate implementation of a suitable graded possibilistic model. An interesting application is found in automated segmentation of diagnostic medical images, where the model provides an interactive visualization tool for this task