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Cluster and classification techniques for the biosciences /
Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these imp...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; New York :
Cambridge University Press,
©2007.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Fielding, Alan. | |
| 245 | 1 | 0 | |a Cluster and classification techniques for the biosciences / |c Alan H. Fielding. |
| 260 | |a Cambridge, UK ; |a New York : |b Cambridge University Press, |c ©2007. | ||
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| 504 | |a Includes bibliographical references (pages 224-239) and index. | ||
| 505 | 0 | |a Exploratory data analysis -- Cluster analysis -- Introduction to classification -- Classification algorithms -- Other classification methods -- Classification accuracy. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential. | ||
| 546 | |a English. | ||
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| 650 | 0 | |a Biology |x Data processing. | |
| 650 | 0 | |a Biology |v Classification. | |
| 650 | 0 | |a Cluster analysis. | |
| 650 | 0 | |a Multivariate analysis. | |
| 650 | 1 | 2 | |a Cluster Analysis |
| 650 | 2 | 2 | |a Biometry |x methods |
| 650 | 2 | 2 | |a Classification |x methods |
| 650 | 2 | 2 | |a Data Interpretation, Statistical |
| 650 | 2 | 2 | |a Multivariate Analysis |
| 650 | 6 | |a Biologie |x Informatique. | |
| 650 | 6 | |a Biologie |v Classification. | |
| 650 | 6 | |a Classification automatique (Statistique) | |
| 650 | 6 | |a Analyse multivariée. | |
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| 650 | 7 | |a SCIENCE |x Life Sciences |x Biology. |2 bisacsh | |
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| 650 | 7 | |a Biology |2 fast | |
| 650 | 7 | |a Biology |x Data processing |2 fast | |
| 650 | 7 | |a Cluster analysis |2 fast | |
| 653 | |a CLUSTER ANALYSIS | ||
| 653 | |a CLASSIFICATION | ||
| 653 | |a BIOMETRY | ||
| 653 | |a MULTIVARIATE ANALYSIS | ||
| 653 | |a BIOLOGY | ||
| 653 | |a DATA PROCESSING | ||
| 655 | 7 | |a Classification |2 fast | |
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