Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A rough margin based support vector machine
Information Sciences: an International Journal
Semi-supervised outlier detection based on fuzzy rough C-means clustering
Mathematics and Computers in Simulation
Rough C-means and Fuzzy Rough C-means for Colour Quantisation
Fundamenta Informaticae - Emergent Computing
A multivariate fuzzy system applied for outliers detection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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C-means clustering is a popular technique to classify unlabeled data into dif-ferent categories. Hard c-means (HCM), fuzzy c-means (FCM) and rough c-means (RCM) were proposed for various applications. In this paper a fuzzy rough c-means algorithm (FRCM) is present, which integrates the advantage of fuzzy set theory and rough set theory. Each cluster is represented by a center, a crisp lower approximation and a fuzzy boundary. The Area of a lower approximation is controlled over a threshold T, which also influences the fuzziness of the final partition. The analysis shows the proposed FRCM achieves the trade-off between convergence and speed relative to HCM and FCM. FRCM will de-grade to HCM or FCM by changing the parameter T. One of the advantages of the proposed algorithm is that the membership of clustering results coincides with human's perceptions, which makes the method has a potential application in understandable fuzzy information granulation.