Hardcover. This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described. * Provides a detailed and up-to-date overview on classification and data mining methods. * Focuses on supervised classification algorithms and their applications, including recent research on the combination of classifiers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Semi-Supervised and Unsupervised Machine Learning (Hardcover)
Editor: ISTE Ltd and John Wiley & Sons Inc, London
Fecha de publicaci�n: 2010
Idioma: Ingl�s
ISBN 10: 1848212038
ISBN 13: 9781848212039
Encuadernaci�n: Hardcover
Edici�n: 1� Edici�n
Estado: new

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Sinopsis

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.

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Amparo Albalate,?University of Ulm, Institute of Information Technology, Germany.

Wolfgang Minker, University of Ulm, Institute of Information Technology, Germany.

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