Data on occupational accidents are usually stored in large databases by worker compensation authorities, and by the safety and prevention teams of companies. An analysis of these databases can play an important role in the prevention of accidents and the reduction of risks, but it can be a complex procedure because of the dimensions and complexity of such databases. The SKM (SOM K-Means) method, a two-level clustering system, made up of SOM (Self Organizing Map) and K-Means clustering, has obtained positive results in identifying the dynamics of critical accidents by referring to a database of 1200 occupational accidents that had occurred in the wood industry. The present research has been conducted to validate the recently presented SKM methodology through the analysis of a larger data set of more than 4000 occupational accidents that occurred in Piedmont (Italy), between 2006 and 2013. This work has partitioned the accidents into groups of different accident dynamics families and has quantified the severity and frequency of occurrence of these accidents. The obtained information may be of help to Company Managers and National Authorities to better address preventive measures and policies concerning the clusters that have been identified as being the most critical within a risk-based decision-making framework.
Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry / Comberti, Lorenzo; Demichela, Micaela; Baldissone, Gabriele; Fois, Gianmario; Luzzi, Roberto. - In: SAFETY. - ISSN 2313-576X. - ELETTRONICO. - 4:4(2018), p. 51.
|Titolo:||Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry|
|Data di pubblicazione:||2018|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3390/safety4040051|
|Appare nelle tipologie:||1.1 Articolo in rivista|