Knowledge discovery in astronomical data

Abstract

With the construction and development of ground-based and space-based observatories, astronomical data amount to Terascale, even Petascale. How to extract knowledge from so huge data volume by automated methods is a big challenge for astronomers. Under this situation, many researchers have studied various approaches and developed different softwares to solve this issue. According to the special task of data mining, we need to select an appropriate technique suiting the requirement of data characteristics. Moreover all algorithms have their own pros and cons. We introduce the characteristics of astronomical data, present the taxonomy of knowledge discovery, and describe the functionalities of knowledge discovery in detail. Then the methods of knowledge discovery are touched upon. Finally the successful applications of data mining techniques in astronomy are summarized and reviewed. Facing data avalanche in astronomy, knowledge discovery in databases (KDD) shows its superiority.

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“…The selected data samples were decomposed into J scales using the discrete wavelet transform with CDF 9/7 [22] wavelet as in (2). This wavelet is employed for lossy compression in JPEG 2000 and Dirac compression standards.…”

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