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http://dx.doi.org/10.36498/kbigdt.2021.6.2.29

Comparison of Clustering Techniques in Flight Approach Phase using ADS-B Track Data  

Jong-Chan Park (인하대학교 통계학과)
Heon Jin Park (인하대학교 통계학과)
Publication Information
The Journal of Bigdata / v.6, no.2, 2021 , pp. 29-38 More about this Journal
Abstract
Deviation of route in aviation safety management is a dangerous factor that can lead to serious accidents. In this study, the anomaly score is calculated by classifying the tracks through clustering and calculating the distance from the cluster center. The study was conducted by extracting tracks within 100 km of the airport from the ADS-B track data received for one year. The wake was vectorized using linear interpolation. Latitude, longitude, and altitude 3D coordinates were used. Through PCA, the dimension was reduced to an axis representing more than 90% of the overall data distribution, and k-means clustering, hierarchical clustering, and PAM techniques were applied. The number of clusters was selected using the silhouette measure, and an abnormality score was calculated by calculating the distance from the cluster center. In this study, we compare the number of clusters for each cluster technique, and evaluate the clustering result through the silhouette measure.
Keywords
anomaly detection; ADS-B track data; clustering;
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