We explore different methods of data mining in the field of aviation and their effectiveness. The field of aviation is always searching for new ways to improve safety. However, due to the large amounts of aviation data collected daily, parsing through it all by hand would be impossible. Because of this, problems are often found by investigating accidents. With the relatively new field of data mining we are able to parse through an otherwise unmanageable amount of data to find patterns and anomalies that indicate potential incidents before they happen. The data mining methods outlined in this paper include Multiple Kernel Learning algorithms, Hidden Markov Models, Hidden Semi-Markov Models, and Natural Language Processing.
Pagels, David A.
"Aviation Data Mining,"
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal: Vol. 2
, Article 3.
Available at: http://digitalcommons.morris.umn.edu/horizons/vol2/iss1/3