Scientific Data Mining
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-0-89871-675-7 (ISBN)
Technological advances are enabling scientists to collect vast amounts of data in fields such as medicine, remote sensing, astronomy, and high-energy physics. These data arise not only from experiments and observations, but also from computer simulations of complex phenomena. As a result, it has become impractical to manually analyze and understand the data. This book describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains and uses them to define an end-to-end process of scientific data mining. This multi-step process includes tasks such as processing the raw image or mesh data to identify objects of interest; extracting relevant features describing the objects; detecting patterns among the objects; and displaying the patterns for validation by the scientists.
Chandrika Kamath is a researcher at Lawrence Livermore National Laboratory, where she is involved in the analysis of data from scientific simulations, observations, and experiments. Her interests include signal and image processing, machine learning, pattern recognition, and statistics, as well as the application of data mining techniques to the solution of practical problems.
Preface; 1. Introduction; 2. Data mining in science and engineering; 3. Common themes in mining scientific data; 4. The scientific data mining process; 5. Reducing the size of the data; 6. Fusing different data modalities; 7. Enhancing image data; 8. Finding objects in the data; 9. Extracting features describing the objects; 10. Reducing the dimension of the data; 11. Finding patterns in the data; 12. Visualizing the data and validating the results; 13. Scientific data mining systems; 14. Lessons learned, challenges, and opportunities; Bibliography; Index.
| Erscheint lt. Verlag | 4.6.2009 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 178 x 255 mm |
| Gewicht | 530 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Naturwissenschaften | |
| ISBN-10 | 0-89871-675-6 / 0898716756 |
| ISBN-13 | 978-0-89871-675-7 / 9780898716757 |
| Zustand | Neuware |
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