Analysing flight data using clustering methods

C. Jesse, Honghai Liu, Edward Smart, David J. Brown

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


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.
Original languageEnglish
Title of host publicationKnowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I
EditorsI. Lovrek, R. Howlett, L. Jain
Place of PublicationBerlin
Number of pages8
ISBN (Print)9783540855620
Publication statusPublished - 2008

Publication series

NameLecture notes in artificial intelligence


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