Rapid prototyping of fuzzy models based on hierarchical clustering
Fuzzy model identification
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Logic-A Modern Perspective
IEEE Transactions on Knowledge and Data Engineering
Fuzzy fractal dimensions and fuzzy modeling
Information Sciences: an International Journal
Pharmacokinetic application of fuzzy structure identification and reasoning
Information Sciences: an International Journal - Special issue: Medical expert systems
Knowledge discovery by a neuro-fuzzy modeling framework
Fuzzy Sets and Systems
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Mining typical patterns from databases
Information Sciences: an International Journal
T-S Fuzzy Model Identification Based on Chaos Optimization
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
Information Sciences: an International Journal
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Hybrid approach for context-aware service discovery in healthcare domain
Journal of Computer and System Sciences
Information Sciences: an International Journal
Granular Computing and Human-Centricity in Computational Intelligence
International Journal of Software Science and Computational Intelligence
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Fuzzy systems approximate highly nonlinear systems by means of fuzzy ''if-then'' rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and offer a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classification problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches.