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

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2015

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Moura, Raphael
Beer, Michael
Kruse, Rudolf

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2015 IEEE Symposium Series on Computational Intelligence. Piscataway, New Jersey, USA: IEEE, 2015, pp. 1838-1843. ISBN 978-1-4799-7560-0. Available under: doi: 10.1109/SSCI.2015.256

Zusammenfassung

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.

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2015 IEEE Symposium Series on Computational Intelligence (SSCI), 7. Dez. 2015 - 10. Dez. 2015, Cape Town, South Africa
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ISO 690MOURA, Raphael, Christoph DOELL, Michael BEER, Rudolf KRUSE, 2015. A Clustering Approach to a Major-Accident Data Set : Analysis of Key Interactions to Minimise Human Errors. 2015 IEEE Symposium Series on Computational Intelligence (SSCI). Cape Town, South Africa, 7. Dez. 2015 - 10. Dez. 2015. In: 2015 IEEE Symposium Series on Computational Intelligence. Piscataway, New Jersey, USA: IEEE, 2015, pp. 1838-1843. ISBN 978-1-4799-7560-0. Available under: doi: 10.1109/SSCI.2015.256
BibTex
@inproceedings{Moura2015Clust-44703,
  year={2015},
  doi={10.1109/SSCI.2015.256},
  title={A Clustering Approach to a Major-Accident Data Set : Analysis of Key Interactions to Minimise Human Errors},
  isbn={978-1-4799-7560-0},
  publisher={IEEE},
  address={Piscataway, New Jersey, USA},
  booktitle={2015 IEEE Symposium Series on Computational Intelligence},
  pages={1838--1843},
  author={Moura, Raphael and Doell, Christoph and Beer, Michael and Kruse, Rudolf}
}
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