Partitional Clustering Algorithms (English Edition) - neues Buch

EAN (ISBN-13): 9783319092591
ISBN (ISBN-10): 3319092596
Erscheinungsjahr: 2014
Herausgeber: Celebi, M. Emre, Springer

Buch in der Datenbank seit 2015-04-17T18:43:18+02:00 (Zurich)
Detailseite zuletzt geändert am 2023-11-17T16:38:24+01:00 (Zurich)
ISBN/EAN: 9783319092591

ISBN - alternative Schreibweisen:
3-319-09259-6, 978-3-319-09259-1
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: celebi
Titel des Buches: algorithms, partition

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Daten vom Verlag:

Autor/in: M. Emre Celebi
Titel: Partitional Clustering Algorithms
Verlag: Springer; Springer International Publishing
415 Seiten
Erscheinungsjahr: 2014-11-07
Cham; CH
Gedruckt / Hergestellt in Vereinigte Staaten.
Sprache: Englisch
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.