Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	September 2016
Genre:	Naturwissensch., Medizin, Technik
	B / Communications Engineering, Networks / Computer networking & communications / Computers / Database Management System / Digital and Analog Signal Processing / Digitale Signalverarbeitung (DSP) / Electrical Engineering / engineering / Image processing / Imaging systems & technology / Information Systems and Communication Service / Signal Processing / Signal, Image and Speech Processing / Speech processing systems mehr...
ISBN:	9783319347981
EAN-Code:	9783319347981
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	6438 gr
Seiten:	415
Illustration:	X, 415 p. 78 illus., 45 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.