Second Order Fuzzy Measure and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images
Fundamenta Informaticae
Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation
Transactions on Rough Sets IX
Fuzzy-rough sets for information measures and selection of relevant genes from microarray data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Rough sets for selection of molecular descriptors to predict biological activity of molecules
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Shadowed sets in the characterization of rough-fuzzy clustering
Pattern Recognition
A rough set approach to spatio-temporal outlier detection
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Improving feature space based image segmentation via density modification
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Crisp and soft clustering of mobile calls
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Rough-fuzzy c-means for clustering microarray gene expression data
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An improved generalized fuzzy c-means clustering algorithm based on GA
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Second Order Fuzzy Measure and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images
Fundamenta Informaticae
Hybrid softcomputing model for lesion identification and information combination: some case studies
International Journal of Data Mining and Bioinformatics
Generalized rough fuzzy c-means algorithm for brain MR image segmentation
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Correlating Fuzzy and Rough Clustering
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
On rough set based non metric model
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
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Rough set based fuzzy k-modes for categorical data
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Title Natural computing: A problem solving paradigm with granular information processing
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Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data
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Rough intuitionistic fuzzy C-means algorithm and a comparative analysis
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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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A generalized hybrid unsupervised learning algorithm, which is termed as rough-fuzzy possibilistic C-means (RFPCM), is proposed in this paper. It comprises a judicious integration of the principles of rough and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. It incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy C-means and the coincident clusters of PCM. The concept of crisp lower bound and fuzzy boundary of a class, which is introduced in the RFPCM, enables efficient selection of cluster prototypes. The algorithm is generalized in the sense that all existing variants of C-means algorithms can be derived from the proposed algorithm as a special case. Several quantitative indices are introduced based on rough sets for the evaluation of performance of the proposed C-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated both qualitatively and quantitatively on a set of real-life data sets.