A Clustering Approach to a Major-Accident Data Set: Analysis of Key Interactions to Minimise Human Errors

Moura, Raphael ORCID: 0000-0003-3494-5945, Beer, Michael ORCID: 0000-0002-0611-0345, Doell, Christoph and Kruse, Rudolf (2015) A Clustering Approach to a Major-Accident Data Set: Analysis of Key Interactions to Minimise Human Errors. In: 2015 IEEE Symposium Series on Computational Intelligence (SSCI), 2015-12-7 - 2015-12-10, Cape Town, South Africa.

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Abstract

This work aims to scrutinise a proprietary dataset containing major accidents occurred in high-Technology facilities, in order to disclose relevant features and indicate a path to the recognition of the genesis of human errors. The application of a tailored Hierarchical Agglomerative Clustering method will provide means to understand data and identify key similarities among accidents and significant interfaces between human factors, the organisational environment and the technology. Conclusions to improve the human performance based on the clustering results are then discussed.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	3 Good Health and Well Being
Depositing User:	Symplectic Admin
Date Deposited:	06 May 2016 14:37
Last Modified:	15 Mar 2024 05:28
DOI:	10.1109/SSCI.2015.256
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3000808