Data clustering : algorithms and applications /

Contributor(s):

Material type: Text

TextSeries: Chapman & Hall/CRC data mining and knowledge discovery seriesPublisher: Boca Raton : Chapman and Hall/CRC, [2014]Description: xxv1, 622 pages : illustrations ; 26 cmISBN:

9781466558212
1466558210

Subject(s):

DDC classification:

519.535

Summary: "Clustering is a diverse topic, and the underlying algorithms depend greatly on the data domain and problem scenario. This book focuses on three primary aspects of data clustering: the core methods such as probabilistic, density-based, grid-based, and spectral clustering etc; different problem domains and scenarios such as multimedia, text, biological, categorical, network, and uncertain data as well as data streams; and different detailed insights from the clustering process because of the subjectivity of the clustering process, and the many different ways in which the same data set can be clustered"-- Provided by publisher.

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	Botho University Botswana Open Shelves	Faculty Business & Accounting	519.535 DAT (Browse shelf(Opens below))	Available		BU-LIB22609
Books	Botho University Botswana Open Shelves	Faculty Business & Accounting	519.535 DAT (Browse shelf(Opens below))	Available		BU-LIB22607

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Previous	519.52 LOH Sampling : design and analysis /	519.53 MUI Aspects of multivariate statistical theory /	519.535 DAT Data clustering : algorithms and applications /	519.535 DAT Data clustering : algorithms and applications /	519.535 HAI Multivariate data analysis /	519.535 HAI Multivariate data analysis /	519.535 MAR Statistics in MATLAB : a primer /	Next

Includes bibliographical references (pages 602-605) and index.

"Clustering is a diverse topic, and the underlying algorithms depend greatly on the data domain and problem scenario. This book focuses on three primary aspects of data clustering: the core methods such as probabilistic, density-based, grid-based, and spectral clustering etc; different problem domains and scenarios such as multimedia, text, biological, categorical, network, and uncertain data as well as data streams; and different detailed insights from the clustering process because of the subjectivity of the clustering process, and the many different ways in which the same data set can be clustered"-- Provided by publisher.

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