Data mining: concepts and techniques
Data mining: concepts and techniques
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Decision Trees: An Overview and Their Use in Medicine
Journal of Medical Systems
Robotics and Computer-Integrated Manufacturing
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
A case study of applying data mining techniques in an outfitter's customer value analysis
Expert Systems with Applications: An International Journal
Data mining source code for locating software bugs: A case study in telecommunication industry
Expert Systems with Applications: An International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Older drivers and accidents: A meta analysis and data mining application on traffic accident data
Expert Systems with Applications: An International Journal
Applying data mining to learn system dynamics in a biological model
Expert Systems with Applications: An International Journal
Simulation data mining for supporting bridge design
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Hi-index | 12.05 |
This article presents an application of data mining method on finite element data and crashworthiness result data of an occupant restraint system. According to the characteristics of the CAE (Computer-Aided Engineering) data, a framework for data preparation is developed based on object-oriented programming concepts. Training sets are built from data recorded in 98 crash simulations that adhere to FMVSS208, the America occupant crash protection testing standard. Relationship between design parameters and system effectiveness is implied in these data sets. Decision tree using C4.5 algorithm and attribute selection method based on attribute's estimated importance are introduced to perform data mining on the building of training sets. The result yielded by data mining endows us with a deeper insight into the interrelations between the key design parameters and the performance of the occupant restraint system in crash simulations. Finally, the learned rules are tested on the real crash simulation data sets. The result of the testing shows that these rules are proper, and can been used as a guidance for the design of the occupant restraint system.