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Titlebook: Data Mining for Scientific and Engineering Applications; Robert L. Grossman,Chandrika Kamath,Raju R. Nambur Book 2001 Springer Science+Bus

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書目名稱Data Mining for Scientific and Engineering Applications
編輯Robert L. Grossman,Chandrika Kamath,Raju R. Nambur
視頻videohttp://file.papertrans.cn/263/262951/262951.mp4
叢書名稱Massive Computing
圖書封面Titlebook: Data Mining for Scientific and Engineering Applications;  Robert L. Grossman,Chandrika Kamath,Raju R. Nambur Book 2001 Springer Science+Bus
描述Advances in technology are making massive data sets common inmany scientific disciplines, such as astronomy, medical imaging,bio-informatics, combinatorial chemistry, remote sensing, and physics.To find useful information in these data sets, scientists andengineers are turning to data mining techniques. This book is acollection of papers based on the first two in a series of workshopson mining scientific datasets. It illustrates the diversity ofproblems and application areas that can benefit from data mining, aswell as the issues and challenges that differentiate scientific datamining from its commercial counterpart. While the focus of the book ison mining scientific data, the work is of broader interest as many ofthe techniques can be applied equally well to data arising in businessand web applications. ..Audience:. This work would be an excellent text for students andresearchers who are familiar with the basic principles of data miningand want to learn more about the application of data mining to theirproblem in science or engineering.
出版日期Book 2001
關鍵詞algorithms; bioinformatics; classification; clustering; computer science; data analysis; data mining; datab
版次1
doihttps://doi.org/10.1007/978-1-4615-1733-7
isbn_softcover978-1-4020-0114-7
isbn_ebook978-1-4615-1733-7Series ISSN 1569-2698 Series E-ISSN 2468-8738
issn_series 1569-2698
copyrightSpringer Science+Business Media Dordrecht 2001
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Keep Out, or Else: Diary as Body in , and ted and to offer the users a more useful and manageable product. This migration of activities faces several technical and human-factor challenges. As data reduction and mining algorithms are often quite specific to the user’s research needs, the user’s algorithm must be integrated virtually unchange
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Graphic Memoirs — autobiografische Comicso much of the work in the traditional data-mining community. We first describe the basic system and follow with a discussion of ongoing work, focusing on efforts in multiscale feature detection and progressive access. Finally, we demonstrate the system for a two-dimensional vector field derived from
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1569-2698 ers who are familiar with the basic principles of data miningand want to learn more about the application of data mining to theirproblem in science or engineering.978-1-4020-0114-7978-1-4615-1733-7Series ISSN 1569-2698 Series E-ISSN 2468-8738
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Robert L. Grossman,Chandrika Kamath,Raju R. Nambur
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