Multiple-resolution clustering for recursive divide and conquer
Steven Noel, Harold H Szu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
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.