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Partitional Clustering Algorithms - M. Emre Celebi
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M. Emre Celebi:

Partitional Clustering Algorithms - encadernada, livro de bolso

2014, ISBN: 3319092588

[EAN: 9783319092584], Neubuch, [PU: Springer International Publishing Nov 2014], ELEKTROTECHNIK; CENTERBASEDCLUSTERING; FLATCLUSTERING; FUZZYC-MEANS; NONHIERARCHICALCLUSTERING; OBJECTIVEF… mais…

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ISBN: 9783319092584

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Partitional Clustering Algorithms - Celebi, M. Emre (Herausgeber)
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Celebi, M. Emre (Herausgeber):
Partitional Clustering Algorithms - encadernada, livro de bolso

2014

ISBN: 3319092588

Gebundene Ausgabe Elektrotechnik, Ingenieurwissenschaft - Ingenieurwissenschaftler, Maschinenbau, Nachrichtentechnik, Kommunikation (Technik) / Telekommunikation, Telekommunikation, Info… mais…

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Partitional Clustering Algorithms - Celebi, M. Emre
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Celebi, M. Emre:
Partitional Clustering Algorithms - encadernada, livro de bolso

2014, ISBN: 3319092588

[EAN: 9783319092584], Neubuch, [PU: Springer International Publishing], ELEKTROTECHNIK INGENIEURWISSENSCHAFT - INGENIEURWISSENSCHAFTLER MASCHINENBAU NACHRICHTENTECHNIK KOMMUNIKATION (TECH… mais…

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Partitional Clustering Algorithms - encadernada, livro de bolso

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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Partitional Clustering Algorithms
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9783319092584


This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.

Dados detalhados do livro - Partitional Clustering Algorithms


EAN (ISBN-13): 9783319092584
ISBN (ISBN-10): 3319092588
Livro de capa dura
Ano de publicação: 2014
Editor/Editora: Springer International Publishing

Livro na base de dados desde 2014-10-10T02:00:28-03:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2023-11-17T12:38:23-03:00 (Sao Paulo)
Número ISBN/EAN: 9783319092584

Número ISBN - Ortografia alternativa:
3-319-09258-8, 978-3-319-09258-4
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: celebi
Título do livro: algorithms, partition

Dados da editora

Autor: M. Emre Celebi
Título: Partitional Clustering Algorithms
Editora: Springer; Springer International Publishing
415 Páginas
Ano de publicação: 2014-11-20
Cham; CH
Impresso / Feito em
Peso: 8,403 kg
Língua: Inglês
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.

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