Abstract
Wavelet analysis is a practical tool to study signal analysis and image processing. Traditional Fourier transform can also transfer the signal into frequency domain, but wavelet analysis is more attractive for its features of multi-resolution and localization of frequency. Recently, there has been significant development in the use of wavelet methods in the data mining process. However, the objective of the study described in this paper is twofold: designing a wavelet transform algorithm on the multiprocessor architecture and using this algorithm in mining spatial outliers of meteorological data. Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Outlier detection reveals important and valuable information from large spatial data sets. As region outliers are commonly multi-scale objects, wavelet analysis is an effective tool to study them. In this paper, we present a wavelet based approach and its applicability in outlier detection. We design a suite of algorithms to effectively discover region outliers and also a parallel algorithm is designed to bring efficiency and speedup for the wavelet analysis. The applicability and effectiveness of the developed algorithms are evaluated on real-world meteorological dataset.