On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Weighted fuzzy pattern matching
Fuzzy Sets and Systems - Mathematical Modelling
Fuzzy Sets and Systems
Towards general measures of comparison of objects
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Generalized fuzzy indices for similarity matching
Fuzzy Sets and Systems - Special issue on clustering and learning
The functional equations of Frank and Alsina for uninorms and nullnorms
Fuzzy Sets and Systems
Similarity and compatibility in fuzzy set theory: assessment and applications
Similarity and compatibility in fuzzy set theory: assessment and applications
Fuzzy Sets and Systems
Aggregation operators: new trends and applications
Aggregation operators: new trends and applications
A k-order fuzzy OR operator for pattern classification with k -order ambiguity rejection
Fuzzy Sets and Systems
On the transitivity of a parametric family of cardinality-based similarity measures
International Journal of Approximate Reasoning
Aggregation Functions (Encyclopedia of Mathematics and its Applications)
Aggregation Functions (Encyclopedia of Mathematics and its Applications)
Information Sciences: an International Journal
Aggregation functions: Construction methods, conjunctive, disjunctive and mixed classes
Information Sciences: an International Journal
A family of measures for best top-n class-selective decision rules
Pattern Recognition
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A common problem in decision-making is to analyze a tuple of numerical values associated with options, such as the degree of satisfaction assigned by experts to alternatives or probability values for hypotheses computed from data. With no loss of generality, it is assumed that the tuple contains values in the unit interval. For post-processing of typical value(s), singular values that may arise from noise in the data, or from unreliable experts, must not be taken into account. We present the concept of block similarity to address the problem of detecting subset(s) of typical values instead of extracting singular ones. The concept relies on suitable aggregation operators that combine the tuple components. Three different block similarity operators are proposed and discussed. These rely on Sugeno integrals of the tuple with respect to three different measures, namely cardinal weighting, symmetric kernel weighting and non-linear weighting. Numerical examples demonstrate their behaviors and their ability to detect blocks of similar values.