Analysing flight data using clustering methods
Research output: Chapter in Book/Report/Conference proceeding › Chapter (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 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 |
Related information
Impacts
REF2014 Impact Case Study: A World First in Flight Safety: University of Portsmouth Academics Bring Avionic Data Analysis into the 21st Century
Impact: Economic & Commercial Impacts
REF2014 Impact Case Study: A World First in Flight Safety: University of Portsmouth Academics Bring Avionic Data Analysis into the 21st Century
Impact: Economic & Commercial Impacts
ID: 61445
