Applying a decision support system for accident analysis by using data mining approach: A case study on one of the Iranian manufactures

Uncertain and stochastic states have been always taken into consideration in the fields of accident and risk management, and have made decision making a difficult and complicated job for managers in corrective action selection and control measure approach. in this article, big data sets concerning accidents occurred in a manufacturing unit have been studied by applying data mining tools. first, the data was preprocessed and then, effective features in an accident were selected while consulting with industry experts and considering production process information. by performing clustering methods, data was divided into separate clusters and by using dunn index as validator of clustering, optimum number of clusters has been determined. in the next stage, by using the apriori algorithm as one of association rule methods, the relations between these fields were identified and the association rules among them were extracted and analyzed. since managers need precise information for decision making, data mining methods, when to be used properly, may act as a suitable decision supporting system.