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Data Mining: Foundations and Intelligent Paradigms [electronic resource] : Volume 3: Medical, Health, Social, Biological and other Applications / edited by Dawn E. Holmes, Lakhmi C Jain.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Intelligent Systems Reference Library ; 25 | Intelligent Systems Reference Library ; 25Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2012Descripción: XVI, 364 p. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9783642231513
Trabajos contenidos:
  • SpringerLink (Online service)
Tema(s): Formatos físicos adicionales: Sin títuloClasificación CDD:
  • 006.3 23
Clasificación LoC:
  • Q342
Recursos en línea:
Contenidos:
Springer eBooksResumen: 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.
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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.

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.

ZDB-2-ENG

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