Description

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.

Semi-Supervised and Unsupervised Machine Learning: Novel Strategies

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£138.95

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Hardback by Amparo Albalate , Wolfgang Minker

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This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on... Read more

    Publisher: ISTE Ltd and John Wiley & Sons Inc
    Publication Date: 14/12/2010
    ISBN13: 9781848212039, 978-1848212039
    ISBN10: 1848212038

    Number of Pages: 320

    Non Fiction , Computing

    Description

    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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