Developing an Enterprise Diagnostic Index System Based on Interval-Valued Hesitant Fuzzy Clustering

Global economic integration drives the development of dynamic competition. In a competitive environment, ever-changing customer demands and technology directly affect leadership core competence enterprises. Therefore, assessing performance enterprises in timely manner is necessary to adjust business activities completely adapt new changes. Enterprise diagnosis scientific tool for judging status enterprises, building rational index system key enterprise diagnosis. Considering large number diagnostic indicators high similarity among indicators, this study proposes selection method based on interval-valued hesitant fuzzy clustering by comparing existing indicator systems. First, organizations are considered as starting point. Through analysis relevant domestic foreign diagnosis, candidate constructed from three aspects, namely performance, employee health, social benefit. view ambiguity inconsistency expert judgment, an set characteristics sets evaluation. For improving entropy function, measurement formula considering information features designed avoid problem data length improve degree identification indicators. Then, similarity, equivalence, truncation matrices constructed, used eliminate redundant with repeated information. The availability proposed illustrated via example, found. Finally, advantages discussed using comparative methods. A comprehensive was constructed. can be basis diagnosing providing objective effective reference.