Mining for Information in Accident Data
FEDERAL AVIATION ADMINISTRATION OKLAHOMA CITY OK CIVIL AEROMEDICAL INST
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Purpose. This project evaluated WinMine, an analytic tool developed by Chickering, Heckerman, Meek, Platt, and Thiesson 2000 to determine its usefulness for identifying higher-order relationships in research data from dynamic and high-consequence aviation events. Traditionally, researchers have relied on several types of analyses to better understand the relationships between factors related to an outcome. However, researchers need an analytic approach that can clearly illustrate the interactions among causal factors as probabilities associated with the chain of events. Method. A convenience sample of aviation accident data previously classified using the Human Factors Analysis and Classification System HFACS Shappell Wiegmann, 2000 2001 was used to evaluate WinMine in contrast to traditional methods, such as bar graphs, contingency tables, and odds ratios. Results. WinMine showed an advantage when compared with other methods because it graphs quantifiable interrelationships between factors and illuminates the underlying hierarchical structure of variables. Discussion. Each technique examined contributed toward understanding the causal factors however, WinMine provided a better picture of the factor interrelationships than the other methods.
- Commercial and General Aviation
- Statistics and Probability
- Computer Hardware