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3 April 1997 Multiple-resolution clustering for recursive divide and conquer
Steven E. Noel, Harold H. Szu
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Proceedings Volume 3078, Wavelet Applications IV; (1997) https://doi.org/10.1117/12.271725
Event: AeroSense '97, 1997, Orlando, FL, United States
Abstract
In recent work, a recursive divide-and-conquer approach was developed for path-minimization problems such as the traveling salesman problem (TSP). The approach is based on multiple-resolution clustering to decompose a problem into minimally-dependent parts. It is particularly effective for large-scale, fractal data sets, which exhibit clustering on all scales, and hence at all resolutions. This leads to the application of wavelets for performing the necessary multiple-resolution clustering. While the general topic of multiple-resolution clustering via wavelets is relatively immature, it has been explored for certain specific applications. However, nothing in the literature addresses the specific type of multiple-resolution clustering needed for the divide-and-conquer approach. That is the primary goal of this paper.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Steven E. Noel and Harold H. Szu "Multiple-resolution clustering for recursive divide and conquer", Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); https://doi.org/10.1117/12.271725
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KEYWORDS
Wavelets
Fractal analysis
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