Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
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
Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation
IEEE Transactions on Knowledge and Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis
IEEE Transactions on Knowledge and Data Engineering
Approximation spaces and information granulation
Transactions on Rough Sets III
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
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
Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Rough intuitionistic fuzzy C-means algorithm and a comparative analysis
Proceedings of the 6th ACM India Computing Convention
Relational Operations and Uncertainty Measure in Rough Relational Database
Fundamenta Informaticae
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
| Hi-index | 0.00 |
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.