Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Journal of Systems and Software
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Risk Analysis Using Extended Fuzzy Cognitive Maps
ICICCI '10 Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics
Re-examination of interestingness measures in pattern mining: a unified framework
Data Mining and Knowledge Discovery
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In the last recent years several approaches to risk assessment and risk management have been adopted to reduce the potential for specific risks in working environments. A safety culture has also developed to let workers acquire knowledge and understanding of risks and safety. Notwithstanding, risks still exist in every workplace. One effective way to improve workers' sensibility to risk, i.e., their ability to effectively assess and control the risks they are exposed to, is risk management training. Unfortunately, people may perceive risks in different ways depending on subjective assessment of the characteristics and severity of the considered risks, and may have tendencies to either take or avoid actions that they feel are risky. Therefore, the knowledge of how workers assess each of the risks they may be exposed to in the workplace is a key factor to conceive effective custom risk management training. In this paper we present a novel approach, based on association rules, to workers' profiling with respect to risk perception and risk propensity in order to provide each of them with specific customized risk management training.