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[EAN: 9783319092584], Neubuch, [PU: Springer International Publishing Nov 2014], ELEKTROTECHNIK; CENTERBASEDCLUSTERING; FLATCLUSTERING; FUZZYC-MEANS; NONHIERARCHICALCLUSTERING; OBJECTIVEF… Plus…
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2014, ISBN: 3319092588
[EAN: 9783319092584], Neubuch, [PU: Springer International Publishing Nov 2014], ELEKTROTECHNIK; CENTERBASEDCLUSTERING; FLATCLUSTERING; FUZZYC-MEANS; NONHIERARCHICALCLUSTERING; OBJECTIVEF… Plus…
ISBN: 9783319092584
[ED: Buch], [PU: Springer, Berlin, Springer International Publishing, Springer], Neuware - This book focuses on partitional clustering algorithms, which are commonly used in engineering a… Plus…
2014
ISBN: 3319092588
Gebundene Ausgabe Elektrotechnik, Ingenieurwissenschaft - Ingenieurwissenschaftler, Maschinenbau, Nachrichtentechnik, Kommunikation (Technik) / Telekommunikation, Telekommunikation, Info… Plus…
2014, ISBN: 3319092588
[EAN: 9783319092584], Neubuch, [PU: Springer International Publishing], ELEKTROTECHNIK INGENIEURWISSENSCHAFT - INGENIEURWISSENSCHAFTLER MASCHINENBAU NACHRICHTENTECHNIK KOMMUNIKATION (TECH… Plus…
2015, ISBN: 3319092588
Partitional Clustering Algorithms ab 39.09 € als gebundene Ausgabe: Auflage 2015. Aus dem Bereich: Bücher, Wissenschaft, Technik, Medien > Bücher, Springer-Verlag GmbH
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Informations détaillées sur le livre - Partitional Clustering Algorithms
EAN (ISBN-13): 9783319092584
ISBN (ISBN-10): 3319092588
Version reliée
Date de parution: 2014
Editeur: Springer International Publishing
Livre dans la base de données depuis 2014-10-10T07:00:28+02:00 (Zurich)
Page de détail modifiée en dernier sur 2023-11-17T16:38:23+01:00 (Zurich)
ISBN/EAN: 9783319092584
ISBN - Autres types d'écriture:
3-319-09258-8, 978-3-319-09258-4
Autres types d'écriture et termes associés:
Auteur du livre: celebi
Titre du livre: algorithms, partition
Données de l'éditeur
Auteur: M. Emre Celebi
Titre: Partitional Clustering Algorithms
Editeur: Springer; Springer International Publishing
415 Pages
Date de parution: 2014-11-20
Cham; CH
Imprimé / Fabriqué en
Poids: 8,403 kg
Langue: Anglais
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
X, 415 p. 78 illus., 45 illus. in color.
BB; Communications Engineering, Networks; Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik; Nachrichtententechnik, Telekommunikation; Verstehen; Center Based Clustering; Flat Clustering; Fuzzy c-means; Nonhierarchical Clustering; Objective Function Based Clustering; Partitional Clustering; Unsupervised Classification; Unsupervised Learning; k-means; Information Systems and Communication Service; Signal, Image and Speech Processing; Communications Engineering, Networks; Computer Communication Networks; Signal, Speech and Image Processing; Netzwerk-Hardware; Elektronik; Digitale Signalverarbeitung (DSP); EA; BC
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering.Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in realistic applications;Discusses algorithms specifically designed for partitional clustering;Covers center-based, competitive learning, density-based, fuzzy, graph-based, grid-based, metaheuristic, and model-based approaches.Autres livres qui pourraient ressembler au livre recherché:
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9783319347981 Partitional Clustering Algorithms (Celebi, M. Emre)
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