A combined approach for the analysis of large occupational accident databases to support accident-prevention decision making

摘要

Occupational accidents are commonly collected in large databases by National Workers Compensation Authorities and companies' safety and prevention teams. The analysis of the data can be difficult because the database elements are characterized by many parameters, which are not of a numerical nature. Data mining techniques could represent an efficient tool for the identification of useful information in large databases. In 2011, a two-level clustering method, made of SOM and numerical clustering, obtained positive results in identifying critical accident dynamics. The present research proceeds from that initial methodology.