Data clustering : algorithms and applications / [edited by] Charu C. Aggarwal, Chandan K. Reddy.
Contributor(s): Aggarwal, Charu C [editor of compilation.] | Reddy, Chandan K [editor of compilation.].
Material type:
TextSeries: Chapman & Hall/CRC data mining and knowledge discovery series.Publisher: London : CRC Press, c2014Description: xxv1, 622 pages : illustrations ; 26 cm.ISBN: 9781466558212 (hardback).Subject(s): Document clustering | Cluster analysis | Data mining | Machine theory | File organization (Computer science) | BUSINESS & ECONOMICS / Statistics | COMPUTERS / Database Management / Data Mining | COMPUTERS / Machine TheoryDDC classification: 519.535 Other classification: BUS061000 | COM021030 | COM037000 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"--
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Books
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Prof. G. K. Chadha Library
South Asian University |
519.535 D2322 (Browse shelf) | Available | BK00011951 |
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"--