Three-way reductions of conflict analysis based on relation matrices and integration measures

Abstract

Conflicts serve as an important focus of uncertainty analysis, and their reductions facilitate the issue identification and conflict solving to become valuable but rare. At present, conflict analysis reductions mainly embrace relation matrices, and they never concern uncertainty measures with highly concentrated information. In this paper, three-way reductions of conflict analysis are transferred from relation matrices to integration measures, and corresponding heuristic reduction algorithms are constructed for information systems. At first, three-way membership degrees and three-way similarity degrees are proposed for conflict analysis, and their measurement boundedness, issue monotonicity, calculation algorithm, and transformation interrelationship are researched. Then, alliance, conflict, and neutrality reductions are proposed based on similarity degrees to acquire heuristic reduction algorithms, and they can be equivalently characterized by both membership degrees and relation matrices. Finally by table examples and data experiments, similarity degrees and relevant measurement properties are validated, and two groups of three-way reduction algorithms related to relation matrices and similarity degrees are comparatively analyzed; as a result, three-way reduction algorithms based on similarity degrees become novel and effective for conflict analysis. This study provides an in-depth insight into three-way reductions of conflict analysis from algebraic measurement.