Analysing flight data using clustering methods

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

Analysing flight data using clustering methods

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

School of Creative Technologies

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

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, Zagreb, Croatia, September 3-5, 2008, proceedings, part I
Editors	I. Lovrek, R. Howlett, L. Jain
Place of Publication	Berlin
Publisher	Springer
Pages	733-740
Number of pages	8
Edition	5177
ISBN (Print)	9783540855620
Publication status	Published - 2008

Publication series

Name	Lecture notes in artificial intelligence
Publisher	Springer
Number	5177

Cite this

Jesse, C., Liu, H., Smart, E., & Brown, D. J. (2008). Analysing flight data using clustering methods. In I. Lovrek, R. Howlett, & L. Jain (Eds.), Knowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I (5177 ed., pp. 733-740). (Lecture notes in artificial intelligence; No. 5177). Springer.

Jesse, C. ; Liu, Honghai ; Smart, Edward et al. / Analysing flight data using clustering methods. Knowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I. editor / I. Lovrek ; R. Howlett ; L. Jain. 5177. ed. Berlin : Springer, 2008. pp. 733-740 (Lecture notes in artificial intelligence; 5177).

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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 = "C. Jesse and Honghai Liu and Edward Smart and Brown, {David J.}",

year = "2008",

language = "English",

isbn = "9783540855620",

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publisher = "Springer",

number = "5177",

pages = "733--740",

editor = "I. Lovrek and R. Howlett and L. Jain",

booktitle = "Knowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I",

edition = "5177",

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Jesse, C, Liu, H , Smart, E & Brown, DJ 2008, Analysing flight data using clustering methods. in I Lovrek, R Howlett & L Jain (eds), Knowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I. 5177 edn, Lecture notes in artificial intelligence, no. 5177, Springer, Berlin, pp. 733-740.

Analysing flight data using clustering methods. / Jesse, C.; Liu, Honghai ; Smart, Edward et al.
Knowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I. ed. / I. Lovrek; R. Howlett; L. Jain. 5177. ed. Berlin: Springer, 2008. p. 733-740 (Lecture notes in artificial intelligence; No. 5177).

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

TY - CHAP

T1 - Analysing flight data using clustering methods

AU - Jesse, C.

AU - Liu, Honghai

AU - Smart, Edward

AU - Brown, David J.

PY - 2008

Y1 - 2008

N2 - 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.

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.

M3 - Chapter (peer-reviewed)

SN - 9783540855620

T3 - Lecture notes in artificial intelligence

SP - 733

EP - 740

BT - Knowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I

A2 - Lovrek, I.

A2 - Howlett, R.

A2 - Jain, L.

PB - Springer

CY - Berlin

ER -

Jesse C, Liu H , Smart E, Brown DJ. Analysing flight data using clustering methods. In Lovrek I, Howlett R, Jain L, editors, Knowledge-based intelligent information and engineering systems: 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, proceedings, part I. 5177 ed. Berlin: Springer. 2008. p. 733-740. (Lecture notes in artificial intelligence; 5177).

Analysing flight data using clustering methods

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