Special Issue on Aggregation Operators and Clustering
Vicenç Torra, Yasuo Narukawa, and Mark Daumas
This issue features decision making and other tools used in artificial intelligence applications. More specifically, the issue includes five papers focused on aggregation operators and clustering.
The series starts with a paper by Yoshida on weighted quasiarithmetic means that focuses on their monotonicity viewed from utility and weighting functions.
In the second paper, Nohmi, Honda and Okazaki focus on trust evaluation for networks, studying matrix operations based on t-norms and t-conorms. The authors also propose fuzzy graphs using adjacent matrices.
These works are followed by three on fuzzy clustering.
Kanzawa, Endo and Miyamoto present a variation of fuzzy c-means based on kernel functions in an approach developed for data with tolerance.
Endo covers clustering using kernel functions. The paper is based on a fuzzy nonmetric model including pairwise constraints in the clustering process.
The concluding paper also uses pairwise constraints, but within agglomerative hierarchical clustering. Hamasuna, Endo and Miyamoto include clusterwise tolerance in their mode.
As the editors of this issue, we would like to thank the referees for their work in the reviews and journal editors-in-chief Profs. Toshio Fukuda and Kaoru Hirota and the journal staff for their support.
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