New data clustering technique and its applications

Chi-Man Kwan; Roger Xu; Leonard S. Haynes

doi:10.1117/12.421060

27 March 2001 New data clustering technique and its applications

Chi-Man Kwan, Roger Xu, Leonard S. Haynes

Proceedings Volume 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III; (2001) https://doi.org/10.1117/12.421060
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Chi-Man Kwan, Roger Xu, and Leonard S. Haynes "New data clustering technique and its applications", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421060

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