A large-group emergency risk decision method based on data mining of public attribute preferences.

Abstract Aiming at the lack of public concern about the attributes of large-group emergency decision making in major emergencies, a large-group emergency risk decision method based on data mining of public attribute preferences is proposed. First, according to historical data from a similar emergency, an attribute-keyword lexicon is established using text mining and latent semantic analysis. Meanwhile, fuzzy association rule mining and a fuzzy cognitive graph are applied to obtain information regarding public opinion about the attributes. Then, a risk measurement model of degree indicators based on information entropy is presented, and according to the linguistic preference for information given by large-group members, the decision risk of large-group members is measured. On this basis, a group member clustering algorithm is constructed to cluster large groups, and then the best alternative is acquired by comparing score function and precise function of the interval intuitionistic fuzzy number. Finally, a case application using historical data is conducted to verify the method's rationality and feasibility. Highlights • A method for obtaining the public opinion on attributes of emergency decision-making alternatives is proposed. • A model of decision risk measurement is proposed. • A clustering algorithm is proposed to deal with large number of decision makers. • With full consideration of controlling decision risk, the large-group emergency risk decision method is proposed. [ABSTRACT FROM AUTHOR]