Fast discovery of association rules
Advances in knowledge discovery and data mining
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Discovery tools for science apps
Communications of the ACM
Mining Classification Rules from Datasets with Large Number of Many-Valued Attributes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
A Scalable Bottum-Up Data Mining Algorithm for Relational Databases
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
The Computer-Aided Discovery of Scientific Knowledge
DS '98 Proceedings of the First International Conference on Discovery Science
Efficient Searches for Similar Subsequences of Different Lengths in Sequence Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
| Hi-index | 0.00 |
This paper presents a sequence pattern mining technique to mine data generated from a wind tunnel experiment. The goal is to discover the nonlinear input-output relationship for a delta wing aircraft. In contrast to categorical datasets, the output variable(s) in this dataset is continuous and takes distinct values, which is common in physical experiments. Directly applying existing decision tree or rule induction mining methods fails to discover sufficient knowledge. Therefore, we propose to extend current techniques by constructing sequence patterns that represent the output variations in certain ranges of selective inputs. Similar sequence patterns are clustered based on a weighted variance measure. Rules can then be derived from similar sequences to predict the output. This technique has been applied to the experimental data and generates rules useful for flight control.