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Data Mining: Foundations and Intelligent Paradigms


Data Mining: Foundations and Intelligent Paradigms

Volume 3: Medical, Health, Social, Biological and other Applications
Intelligent Systems Reference Library, Band 25

von: Dawn E. Holmes, Lakhmi C Jain

149,79 €

Verlag: Springer
Format: PDF
Veröffentl.: 12.01.2012
ISBN/EAN: 9783642231513
Sprache: englisch
Anzahl Seiten: 364

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

<p>There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, Social, Biological and other Applications” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.</p>
From the content: Advances in Data Mining Applications.- Temporal Pattern Mining for Medical Applications.- BioKeySpotter: an Unsupervised Keyphrase Extraction Technique in the Biomedical Full-text Collection.- Mining Health Claims data for assessing patient risk.- Mining biological networks for similar patterns.- Estimation of Distribution Algorithms in Gene Expression Data Analysis.
<p>Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 3 of this three volume series, we have brought together contributions from some of the most prestigious researchers in applied data mining. Areas of application covered are diverse and include healthcare and finance. Each of the chapters is self contained. Statisticians, applied scientists/ engineers and researchers in bioinformatics will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in applied data mining. </p>
Contains the latest research on data mining research and its applications to financial modelling The state of the art of data mining for financial modelling is presented in a handbook style Written by leading experts in this field

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