Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Data Mining to Predict Aircraft Component Replacement
IEEE Intelligent Systems
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Information Systems - Databases: Creation, management and utilization
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Older drivers and accidents: A meta analysis and data mining application on traffic accident data
Expert Systems with Applications: An International Journal
A Decision Support Tool for Health Service Re-design
Journal of Medical Systems
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Hi-index | 12.05 |
For the effective operation of the air power in the modern war, aircraft should be well-maintained by preventing a series of failures. However, the current maintenance system employed by ROKAF (Republic of Korea Air Force) does not fully utilize cumulative sequential failure data. In this paper, we apply sequential association rules to extract the failure patterns and forecast failure sequences of ROKAF aircrafts according to various combinations of aircraft types, location, mission and season. It is expected that our analysis can add value to the existing maintenance database. Also, our approach can improve the utilization of aircrafts by properly forecasting the future demand of aircraft spare parts.