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Proceedings Paper

New data clustering technique and its applications
Author(s): Chi-Man Kwan; Roger Xu; Leonard S. Haynes
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Paper Abstract

A new approach to data clustering is presented in this paper. The approach consists of three steps. First, preprocessing of raw sensor data was performed. Intelligent Automation, Incorporated (IAI) used Fast Fourier Transform (FFT) in the preprocessing stage to extract the significant frequency components of the sensor signals. Second, Principal Component Analysis (PCA) was used to further reduce the dimension of the outputs of the preprocessing stage. PCA is a powerful technique for extracting the features inside the input signals. The dimensionality reduction can reduce the size of the neural network classifier in the next stage. Consequently the training and recognition time will be significantly reduced. Finally, neural network classifier using Learning Vector Quantization (LVQ) is used for data classification. The algorithm was successfully applied to two commercial systems at Boeing: Auxiliary Power Units and solenoid valve system.

Paper Details

Date Published: 27 March 2001
PDF: 5 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421060
Show Author Affiliations
Chi-Man Kwan, Intelligent Automation, Inc. (United States)
Roger Xu, Intelligent Automation, Inc. (United States)
Leonard S. Haynes, Intelligent Automation, Inc. (United States)

Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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