Partitional Clustering Algorithms

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106,99 

ISBN: 3319092588
ISBN 13: 9783319092584
Herausgeber: M Emre Celebi
Verlag: Springer Verlag GmbH
Umfang: x, 415 S., 33 s/w Illustr., 45 farbige Illustr., 415 p. 78 illus., 45 illus. in color.
Erscheinungsdatum: 20.11.2014
Auflage: 1/2014
Format: 2.1 x 24.2 x 16
Gewicht: 732 g
Produktform: Gebunden/Hardback
Einband: Gebunden

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 centerbased, competitive learning, densitybased, fuzzy, graphbased, gridbased, metaheuristic, and modelbased approaches.

Artikelnummer: 6924565 Kategorie:

Beschreibung

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.

Autorenporträt

Dr. Emre Celebi is an Associate Professor with the Department of Computer Science, at Louisiana State University in Shreveport.

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E-Mail: juergen.hartmann@springer.com

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