Abstract
Matched filter techniques are a staple of modern signal and image processing. They provide a firm foundation (both theoretical and empirical) for detecting and classifying patterns in statistically described backgrounds. Application of these methods to databases has become increasingly common in certain fields (e.g. astronomy). This paper describes an algorithm (based on statistical signal processing methods), a software architecture (based on a hybrid layered approach) and a parallelization scheme (based on a client/server model) for finding clusters in large astronomical databases. The method has proved successful in identifying clusters in real and simulated data. The implementation is flexible and readily executed in parallel on a network of workstations.
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Kepner, J., Kim, R. Cluster Detection in Databases: The Adaptive Matched Filter Algorithm and Implementation. Data Mining and Knowledge Discovery 7, 57–79 (2003). https://doi.org/10.1023/A:1021512702360
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DOI: https://doi.org/10.1023/A:1021512702360