Abstract
This paper reviews existing forms of density-based, partitional and hierarchical clustering methods in the context of flight data analysis. Advantages and disadvantages are fully explored with a focus on proposing a clustering-based ensemble framework for monitoring flight data in order to search for anomalies during flight operation. Case studies in selected flight scenarios are provided to demonstrate the potential of clustering methods and their integration with reasoning techniques in detecting abnormal flights.
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© 2008 Springer-Verlag Berlin Heidelberg
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Jesse, C., Liu, H., Smart, E., Brown, D. (2008). Analysing Flight Data Using Clustering Methods. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_92
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DOI: https://doi.org/10.1007/978-3-540-85563-7_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85562-0
Online ISBN: 978-3-540-85563-7
eBook Packages: Computer ScienceComputer Science (R0)