Clus-DOWA: A New Dependent OWA Operator

Aggregation operators are crucial to integrating diverse decision makers' opinion. While minimum and maximum can represent optimistic and pessimistic extremes, an Ordered Weighted Aggregation (OWA)operator is able to reflect varied human attitudes lying between the two using distinct weight vectors. Several weight determination techniques ignore characteristics of data being aggregated. In contrary, data-oriented operators like centered OWA and dependent OWA utilize the centralized data structure to generate reliable weights. Values near the center of a group receive higher weights than those further away. Despite its general applicability, this perspective entirely neglects any local data structures representing strong agreements or consensus. This paper presents a new dependent OWA operator (Clus-DOWA) that applies distributed structure of data or data clusters to determine its weight vector. The reliability of weights created by DOWA and Clus-DOWA operators are experimentally compared in the tasks of classification and unsupervised feature selection.