Applying data mining technology into civil aviation events analysis can help to obtain the core contents quickly. Based on events caused by the crew members, the term frequency-inverse document frequency (TF-IDF) algorithm was used to vectorize the text features and give weights. The K-means algorithm was used to cluster analysis on the vectorized data, and the results obtained by the multidimensional scaling algorithm were visually displayed. Then, statistical analysis of the stages and causes of the events was carried out based on the cluster results. Finally, “mistakes of crew members” was taken as an example to do analysis. The results show that emphases should be paid on the three types of crew caused events, which are “approach suspension/go around”, “incomplete predetermined configuration” and “overspeed.” Furthermore, it proves that text feature extraction and cluster analysis can be applied to do analysis of human-caused events and help to conveniently and quickly clarify the direction of safety risk management.
Text Feature Extraction and Clustering Analysis of Events Caused by the Cockpit Crew
2020-10-14
659383 byte
Conference paper
Electronic Resource
English
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