Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry

Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry

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 p...

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Bibliographic Details
Main Authors: Lorenzo Comberti, Micaela Demichela, Gabriele Baldissone, Gianmario Fois, Roberto Luzzi
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Safety
Subjects:
clustering
SOM
accident database analysis
accident prevention
safety
risk-based decision making
Online Access:https://www.mdpi.com/2313-576X/4/4/51

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