TY  - CONF
T1  - A Clustering Approach to a Major-Accident Data Set: Analysis of Key Interactions to Minimise Human Errors
A1  - Moura, Raphael
A1  - Doell, Christoph
A1  - Beer, Michael
A1  - Kruse, Rudolf
TI  - Symposium Series on Computational Intelligence (SSCI)
Y1  - 2015
IS  - 978-1-4799-7560-0/15
SP  - 1838
EP  - 1843
PB  - IEEE
T2  - IEEE
CY  - Cape Town
M2  - doi: 10.1109/SSCI.2015.256
N2  - 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, using
the bray-curtis dissimilarity and two different linkage functions
– complete and average – will provide means to understand data
and identify key similarities among accidents. Significant interfaces
between human factors, the organisational environment
and the technology will be described. Main clustering results
have shown that accidents featuring communication issues and
interface problems were grouped together, and turned out to
be the most deadly ones, considering the fatality rate. Another
cluster highlighted relevant training shortcomings. Also, design
failures, poor quality control and inadequate task allocation
were identified as key contributing factors to major accidents.
Conclusions to improve the human performance based on these
clustering results are then discussed.
ER  -