Analysing flight data using clustering methods

Christopher Jesse; Honghai Liu; Edward Smart; David Brown

doi:10.1007/978-3-540-85563-7_92

Analysing flight data using clustering methods

Christopher Jesse^*
, Honghai Liu
, Edward Smart
, David Brown

^*Corresponding author for this work

University of Portsmouth

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

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.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings
Publisher	Springer Verlag
Pages	733-740
Number of pages	8
Edition	PART 1
ISBN (Print)	3540855629, 9783540855620
DOIs	https://doi.org/10.1007/978-3-540-85563-7_92
State	Published - 2008
Externally published	Yes
Event	12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb, Croatia Duration: 3 Sep 2008 → 5 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5177 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008
Country/Territory	Croatia
City	Zagreb
Period	3/09/08 → 5/09/08

Access to Document

10.1007/978-3-540-85563-7_92

Cite this

Jesse, C., Liu, H., Smart, E., & Brown, D. (2008). Analysing flight data using clustering methods. In Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings (PART 1 ed., pp. 733-740). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5177 LNAI, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-85563-7_92

Jesse, Christopher ; Liu, Honghai ; Smart, Edward et al. / Analysing flight data using clustering methods. Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1. ed. Springer Verlag, 2008. pp. 733-740 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Analysing flight data using clustering methods",

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.",

author = "Christopher Jesse and Honghai Liu and Edward Smart and David Brown",

year = "2008",

doi = "10.1007/978-3-540-85563-7\_92",

language = "英语",

isbn = "3540855629",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings",

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Jesse, C, Liu, H, Smart, E & Brown, D 2008, Analysing flight data using clustering methods. in Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5177 LNAI, Springer Verlag, pp. 733-740, 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, Zagreb, Croatia, 3/09/08. https://doi.org/10.1007/978-3-540-85563-7_92

Analysing flight data using clustering methods. / Jesse, Christopher; Liu, Honghai; Smart, Edward et al.
Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1. ed. Springer Verlag, 2008. p. 733-740 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5177 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Smart, Edward

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AB - 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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M3 - 会议稿件

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SN - 3540855629

SN - 9783540855620

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Jesse C, Liu H, Smart E, Brown D. Analysing flight data using clustering methods. In Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. PART 1 ed. Springer Verlag. 2008. p. 733-740. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-85563-7_92

Analysing flight data using clustering methods

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