Information fusion based conflict analysis model for multi-source fuzzy data

Abstract

Conflict is ubiquitous in life. Conflict analysis is a tool for understanding conflicts, whose aim is to analyze the conflict situations in data to help decision makers avoid risks. Existing conflict analysis methods mainly focus on single-source data. However, the emergence of big data era has generated more complex data, such as multi-source data obtained from different perspectives, which can capture details that single-source data is missing. Not only that, most data also exhibit characteristics of fuzziness. The above situations make it more challenging to construct a conflict analysis model in the environment of multi-source fuzzy data to acquire a compliant decision. Therefore, conflict analysis for multi-source fuzzy data is a worthy research topic. However, the existing few studies on multi-source fuzzy data either favor attribute values or ignore conflict resolution, which reduces the conflict resolution performance due to underutilizing attribute information. To solve the above problem, we divide the attribute values of multi-source fuzzy data into three attitude intervals to distinguish different attitudes of agents. Then, we propose a function to measure conflict and construct a conflict analysis model for a multi-source fuzzy formal context. Additionally, we put forward an information fusion method based on the minimum of fuzzy entropy, whose purpose is to achieve conflict resolution quickly. Finally, experiments conducted on 18 datasets demonstrate that our information fusion method can achieve conflict resolution effectively, and provide a useful reference for decision-makers.