Abstract :
Uncertain and stochastic states have been always taken into consideration in the fields of risk management and accident, like other fields of industrial engineering, and have made decision making difficult and complicated for managers in corrective action selection and control measure approach. In this research, huge data sets of the accidents of a manufacturing and industrial unit have been studied by applying clustering methods and association rules as data mining methods. First, the accident data was briefly studied. Then, effective features in an accident were selected while consulting with industry experts and considering production process information. By performing clustering method, 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 supporting system.
