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Scientific Data Mining and Knowledge Discovery: Principles and Foundations

by Mohamed Medhat Gaber
Save 35% Save 35%
Current price ₹7,277.00
Original price ₹11,194.00
Original price ₹11,194.00
Original price ₹11,194.00
(-35%)
₹7,277.00
Current price ₹7,277.00

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Book cover type: Paperback
  • ISBN13: 9783642426247
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 400
  • Original Price: EUR 99.99
  • Language: English
  • Edition: 2010
  • Item Weight: 583 grams
  • BISAC Subject(s): Data Science / Data Analytics, Chemistry / Computational & Molecular Modeling, and Earth Sciences / Geology

From the Back Cover

With the evolution in data storage, large databases have stimulated researchers from many areas, especially machine learning and statistics, to adopt and develop new techniques for data analysis in different fields of science. In particular, there have been notable successes in the use of statistical, computational, and machine learning techniques to discover scientific knowledge in the fields of biology, chemistry, physics, and astronomy. With the recent advances in ontologies and knowledge representation, automated scientific discovery (ASD) has further, great prospects in the future.

The contributions in this book provide the reader with a complete view of the different tools used in the analysis of data for scientific discovery. Gaber has organized the presentation into four parts: Part I provides the reader with the necessary background in the disciplines on which scientific data mining and knowledge discovery are based. Part II details applications of computational methods used in geospatial, chemical, and bioinformatics applications. Part III is about data mining applications in geosciences, chemistry, and physics. Finally, in Part IV, future trends and directions for research are explained.

The book serves as a starting point for students and researchers interested in this multidisciplinary field. It offers both an overview of the state of the art and lists areas and open issues for future research and development.

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