RFCM: a hybrid clustering algorithm using rough and fuzzy sets. A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. It comprises a judicious integration of the principles of rough sets 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. The concept of crisp lower bound and fuzzy boundary of a class, introduced in rough-fuzzy c-means, enables efficient selection of cluster prototypes. Several quantitative indices are introduced based on rough sets for evaluating the performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated on a set of real life data sets.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Zhang, Tengfei; Chen, Long; Ma, Fumin: A modified rough c-means clustering algorithm based on hybrid imbalanced measure of distance and density (2014)
- Małyszko, Dariusz; Stepaniuk, Jarosław: Adaptive rough entropy clustering algorithms in image segmentation (2010)
- Mitra, Sushmita; Pedrycz, Witold; Barman, Bishal: Shadowed $c$-means: integrating fuzzy and rough clustering (2010)
- Maji, Pradipta; Kundu, Malay K.; Chanda, Bhabatosh: Second order fuzzy measure and weighted co-occurrence matrix for segmentation of brain MR images (2008)
- Maji, Pradipta; Pal, Sankar K.: RFCM: a hybrid clustering algorithm using rough and fuzzy sets (2007)