Data mining for scientific and engineering applications

Contributor(s):

Grossman, Robert L; Kamath, Chandrika; Kegelmeyer, P

Material type: Text

TextPublication details: Boston Kluwer Academic Publishers 2001Description: 605 pISBN:

1402000332

Subject(s):

Data mining

DDC classification:

006.3

Summary: 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 data sets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenge 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.

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Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad	006.3 D2 (Browse shelf(Opens below))	Available		159349

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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 data sets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenge 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.

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