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"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"--
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Subjects
Cluster analysis, Data mining, Machine theory, File organization (computer science), Document clustering, File organization (Computer science), Data Mining, Regroupement des documents (Informatique), Classification automatique (Statistique), Exploration de données (Informatique), Théorie des automates, Fichiers (Informatique), OrganisationShowing 7 featured editions. View all 7 editions?
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1
Data Clustering: Algorithms and Applications
2018, Taylor & Francis Group
in English
1498785778 9781498785778
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2
Data Clustering: Algorithms and Applications
2018, Taylor & Francis Group
in English
1315373513 9781315373515
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3
Data Clustering: Algorithms and Applications
2018, Taylor & Francis Group
in English
1315362783 9781315362786
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4
Data Clustering: Algorithms and Applications
2018, Taylor & Francis Group
in English
1466558229 9781466558229
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5
Data Clustering: Algorithms and Applications
2018, Taylor & Francis Group
in English
1315360411 9781315360416
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6
Data clustering: algorithms and applications
2014, Chapman and Hall/CRC
in English
1466558210 9781466558212
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Book Details
Edition Notes
Includes bibliographical references (pages 602-605) and index.
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- Created November 13, 2020
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August 4, 2024 | Edited by bitnapper | merge authors |
February 1, 2023 | Edited by ImportBot | import existing book |
December 15, 2022 | Edited by MARC Bot | import existing book |
September 17, 2021 | Edited by ImportBot | import existing book |
November 13, 2020 | Created by MARC Bot | Imported from Library of Congress MARC record |