Clustering Alternatives and Learning Preferences Based on Decision Attitudes and Weighted Overlap Dominance

An initial assessment on a given set of alternatives is necessary for understanding complex decision problems and their possible solutions. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled for understanding and solving the inherent conflict of decision making. This paper revises multi-criteria modeling of imprecise data, inferring outranking and indifference binary relations and classifying alternatives according to their similarity or dependency. After the initial assessment on the set of alternatives, preference orders are built according to the attitudes of decision makers, aiding the decision process by identifying solutions with minimal dissention.