Data mining with decision trees and decision rules
Future Generation Computer Systems - Special double issue on data mining
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Principles of data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Mining Aviation Data to Understand Impacts of Severe Weather On Airspace System Performance
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
A co-evolving decision tree classification method
Expert Systems with Applications: An International Journal
Privacy preserving decision tree learning over multiple parties
Data & Knowledge Engineering
A quantitative model for aviation safety risk assessment
Computers and Industrial Engineering
Editorial: Recent advances in data mining
Engineering Applications of Artificial Intelligence
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
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Data mining methods have been successfully applied to different fields. Aviation industry is one of them. There is a large amount of knowledge and data accumulation in aviation industry. These data could be stored in the form of pilot reports, maintenance reports, incident reports or delay reports. This paper explains the data mining application on the incident reports of the Federal Aviation Administration (FAA) Accident/Incident Data System database, contains incident data records for all categories of civil aviation between the years of 2000 and 2006. In this study, we applied data mining methods on the incident reports. Moreover rough sets concept is used to reduce the attributes of data set. The purpose of this application is to find out the effective attributes in order to reduce the number of the fatality in the incidents. The categorization tools and decision trees are used to find the relations and rules about the incidents resulted in fatality. For this purpose data-mining analysis is conducted. As a result some rules about the fatality are obtained and also the parameters that affect the fatality in the incident have determined. The rules found are tested in terms of their accuracy and reliability, and these results are seen to be meaningful.