On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Sets and Systems
The ordered weighted averaging operators: theory and applications
The ordered weighted averaging operators: theory and applications
Aggregation operators: properties, classes and construction methods
Aggregation operators
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Aggregation Functions: A Guide for Practitioners
Aggregation Functions: A Guide for Practitioners
Similarities in fuzzy data mining: from a cognitive view to real-world applications
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
On the properties of OWA operator weights functions with constant level of orness
IEEE Transactions on Fuzzy Systems
Using Stress Functions to Obtain OWA Operators
IEEE Transactions on Fuzzy Systems
Discrete Choquet Integral as a Distance Metric
IEEE Transactions on Fuzzy Systems
Time Series Smoothing and OWA Aggregation
IEEE Transactions on Fuzzy Systems
Distance and similarity measures for hesitant fuzzy sets
Information Sciences: an International Journal
Fuzzy ordered distance measures
Fuzzy Optimization and Decision Making
To reach consensus using uninorm aggregation operator: A gossip-based protocol
International Journal of Intelligent Systems
On quasi-metric aggregation functions and fixed point theorems
Fuzzy Sets and Systems
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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We describe the basic properties of a norm and introduce the Minkowski norm. We then show that the OWA aggregation operator can be used to provide norms. To enable this we require that the OWA weights satisfy the buoyancy property, wj ≥ wk for j k. We consider a number of different classes of OWA norms. It is shown that the functional generation of the weights of an OWA norm requires the weight generating function have a non-positive second derivative. We discuss the use of the generalized OWA operator to provide norms. Finally we describe the use of OWA operators to induce similarity measures.