Non-Euclidean c-means clustering algorithms
Intelligent Data Analysis
The induced generalized OWA operator
Information Sciences: an International Journal
A time-domain-constrained fuzzy clustering method and its application to signal analysis
Fuzzy Sets and Systems
The induced probabilistic OWA distance and its application in decision making
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
A generalized model between the OWA operator, the weighted average and the probability
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Decision-making with distance measures and induced aggregation operators
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Fuzzy induced generalized aggregation operators and its application in multi-person decision making
Expert Systems with Applications: An International Journal
Decision-making in sport management based on the OWA operator
Expert Systems with Applications: An International Journal
Group decision making with distance measures and probabilistic information
Knowledge-Based Systems
Information Sciences: an International Journal
Fuzzy decision making with induced heavy aggregation operators and distance measures
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms