Partitional Clustering Algorithms (English Edition) - libro nuevo

EAN (ISBN-13): 9783319092591
ISBN (ISBN-10): 3319092596
Año de publicación: 2014
Editorial: Celebi, M. Emre, Springer

Libro en la base de datos desde 2015-04-17T18:43:18+02:00 (Madrid)
Página de detalles modificada por última vez el 2023-11-17T16:38:24+01:00 (Madrid)
ISBN/EAN: 9783319092591

ISBN - escritura alterna:
3-319-09259-6, 978-3-319-09259-1
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: celebi
Título del libro: algorithms, partition

---

Datos del la editorial

Autor: M. Emre Celebi
Título: Partitional Clustering Algorithms
Editorial: Springer; Springer International Publishing
415 Páginas
Año de publicación: 2014-11-07
Cham; CH
Impreso en
Idioma: Inglés
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.