Abstract
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.
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Liu, Z., Chu, W.W., Huang, A., Folk, C., Ho, CM. (2001). Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_30
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DOI: https://doi.org/10.1007/3-540-45357-1_30
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