Partitional Clustering Algorithms - Rilegato

 
9783319092584: Partitional Clustering Algorithms
Rilegato
ISBN 10: 3319092588 ISBN 13: 9783319092584
Casa editrice: Springer Nature, 2014

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

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

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Nature
Data di pubblicazione
2014
Lingua
Inglese
ISBN 10 
3319092588
ISBN 13 
9783319092584
Rilegatura
Copertina rigida
Numero di pagine
415
Redattore
Celebi M. Emre
Contatto del produttore
ProductSafety@springernature.com
ProductSafety@springernature.com

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Darmstadt
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Germania

Altre edizioni note dello stesso titolo

9783319347981: Partitional Clustering Algorithms

Edizione in evidenza

ISBN 10:  3319347985 ISBN 13:  9783319347981
Casa editrice: Springer, 2016
Brossura