Data Mining for Scientific and Engineering Applications:
Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining tec...
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer US
2001
|
| Ausgabe: | 1st ed. 2001 |
| Schriftenreihe: | Massive Computing
2 |
| Schlagworte: | |
| Online-Zugang: | UBY01 URL des Eerstveröffentlichers |
| Zusammenfassung: | Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering |
| Beschreibung: | 1 Online-Ressource (XX, 605 p) |
| ISBN: | 9781461517337 |
| DOI: | 10.1007/978-1-4615-1733-7 |
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| edition | 1st ed. 2001 |
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| spelling | Data Mining for Scientific and Engineering Applications edited by R.L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, R. Namburu 1st ed. 2001 New York, NY Springer US 2001 1 Online-Ressource (XX, 605 p) txt rdacontent c rdamedia cr rdacarrier Massive Computing 2 Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering Data Structures and Information Theory Artificial Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Engineering, general Theory of Computation Data structures (Computer science) Artificial intelligence Statistics Engineering Computers Data Mining (DE-588)4428654-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Data Mining (DE-588)4428654-5 s DE-604 Grossman, R.L. edt Kamath, C. edt Kegelmeyer, P. edt Kumar, V. edt Erscheint auch als Druck-Ausgabe 9781402000331 Erscheint auch als Druck-Ausgabe 9781402001147 Erscheint auch als Druck-Ausgabe 9781461517344 https://doi.org/10.1007/978-1-4615-1733-7 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Data Mining for Scientific and Engineering Applications Data Structures and Information Theory Artificial Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Engineering, general Theory of Computation Data structures (Computer science) Artificial intelligence Statistics Engineering Computers Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4143413-4 |
| title | Data Mining for Scientific and Engineering Applications |
| title_auth | Data Mining for Scientific and Engineering Applications |
| title_exact_search | Data Mining for Scientific and Engineering Applications |
| title_exact_search_txtP | Data Mining for Scientific and Engineering Applications |
| title_full | Data Mining for Scientific and Engineering Applications edited by R.L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, R. Namburu |
| title_fullStr | Data Mining for Scientific and Engineering Applications edited by R.L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, R. Namburu |
| title_full_unstemmed | Data Mining for Scientific and Engineering Applications edited by R.L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, R. Namburu |
| title_short | Data Mining for Scientific and Engineering Applications |
| title_sort | data mining for scientific and engineering applications |
| topic | Data Structures and Information Theory Artificial Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Engineering, general Theory of Computation Data structures (Computer science) Artificial intelligence Statistics Engineering Computers Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Structures and Information Theory Artificial Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Engineering, general Theory of Computation Data structures (Computer science) Artificial intelligence Statistics Engineering Computers Data Mining Aufsatzsammlung |
| url | https://doi.org/10.1007/978-1-4615-1733-7 |
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