Partitional Clustering Algorithms (English Edition) - nouveau livre

EAN (ISBN-13): 9783319092591
ISBN (ISBN-10): 3319092596
Date de parution: 2014
Editeur: Celebi, M. Emre, Springer

Livre dans la base de données depuis 2015-04-17T18:43:18+02:00 (Zurich)
Page de détail modifiée en dernier sur 2023-11-17T16:38:24+01:00 (Zurich)
ISBN/EAN: 9783319092591

ISBN - Autres types d'écriture:
3-319-09259-6, 978-3-319-09259-1
Autres types d'écriture et termes associés:
Auteur du livre: celebi
Titre du livre: algorithms, partition

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Données de l'éditeur

Auteur: M. Emre Celebi
Titre: Partitional Clustering Algorithms
Editeur: Springer; Springer International Publishing
415 Pages
Date de parution: 2014-11-07
Cham; CH
Imprimé / Fabriqué en
Langue: Anglais
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
X, 415 p. 78 illus., 45 illus. in color.

EA; E107; eBook; Nonbooks, PBS / 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; B; Communications Engineering, Networks; Information Systems and Communication Service; Signal, Image and Speech Processing; Communications Engineering, Networks; Computer Communication Networks; Signal, Speech and Image Processing; Engineering; Netzwerk-Hardware; Elektronik; Digitale Signalverarbeitung (DSP); BB

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.