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
Interpretation of clusters in the framework of shadowed sets
Pattern Recognition Letters
Rough-fuzzy clustering: an application to medical imagery
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Shadowed sets: representing and processing fuzzy sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The paper presents a novel application of the shadowed clustering algorithm for uncertainty modeling and CT scan image segmentation. The core, shadowed and the exclusion regions, generated via shadowed c-means (SCM), quantize the ambiguity into three zones. This leads to faster convergence and reduced computational complexity. It is observed that SCM generates the best prototypes even in the presence of noise, thereby producing the best approximation of a structure in the unsupervised mode. A comparison with rough-fuzzy clustering algorithm reveals the automatic determination of the threshold and absence of externally tuned parameters in SCM. Experiments suggest that SCM is better suited for extraction of regions under vascular insult in the brain via pixel clustering. The relative efficacy of SCM in brain infarction diagnosis is validated by expert radiologists.