Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
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
Digital Image Processing
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
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
Shadowed Clustering for Speech Data and Medical Image Segmentation
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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
International Journal of Approximate Reasoning
An extension to rough c-means clustering algorithm based on boundary area elements discrimination
Transactions on Rough Sets XVI
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
A novel application of rough-fuzzy clustering is presented for synthetic as well as CT scan images of the brain. It is observed that the algorithm generates good prototypes even in the presence of outliers. The rough-fuzzy clustering simultaneously handles overlap of clusters and uncertainty involved in class boundary, thereby yielding the best approximation of a given structure in unlabeled data. The number of clusters is automatically optimized in terms of various validity indices. A comparative study is made with related partitive algorithms. Experimental results demonstrate the diagnosis of the extent of brain infarction in CT scan images, and is validated by medical experts.