Abstract #301404

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JSM 2003 Abstract #301404
Activity Number: 281
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #301404
Title: Wavelet-based Data Reduction Procedures for Multiple Functional Data Curves
Author(s): Uk Jung*+ and Jye-Chyi Lu and Myong-kee Jeong
Companies: Georgia Institute of Technology and Georgia Institute of Technology and Georgia Institute of Technology
Address: 328297 Gatech Station, Atlanta, GA, 30332,
Keywords: cluster analysis ; data mining ; estimation ; model selection ; nonstationary data ; optimization
Abstract:

Contributed from the advance data acquisition systems, signal processing and data mining techniques are more popular than ever in many fields including intelligent manufacturing. As data ets increase in size, exploration, manipulation, and analysis become resource consuming. This presentation shows procedures for "reducing the size of data'' in a mathematical rigorous framework. Then, we provide examples of applying existing (and new) procedures to the reduced-size data for various decision-making purposes. In particular, our procedures take the advantage of the lower computation complexity of the discrete wavelet transform compared with many commonly used signal processing procedures. An objective function is formulated to balance the requirements of modeling accuracy, data reduction, and class assignment and separation for multiple data curves. Unlike the feature extraction procedures developed in many engineering fields for a preplanned decision rule, the reduced-size data support all kinds of planned and unplanned decision rules. They also provide accurate reconstruction of the original data with possible sharp jumps difficult to handle.

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