Overview
Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in real-world 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
Includes supplementary material: sn.pub/extras
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Table of contents (12 chapters)
Keywords
About this book
Reviews
“The content of the book is really outstanding in terms of the clarity of the discourse and the variety of well-selected examples. … The book brings substantial contributions to the field of partitional clustering from both the theoretical and practical points of view, with the concepts and algorithms presented in a clear and accessible way. It addresses a wide range of readers, including scientists, students, and researchers.” (L. State, Computing Reviews, April, 2015)
Editors and Affiliations
About the editor
Bibliographic Information
Book Title: Partitional Clustering Algorithms
Editors: M. Emre Celebi
DOI: https://doi.org/10.1007/978-3-319-09259-1
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-09258-4Published: 20 November 2014
Softcover ISBN: 978-3-319-34798-1Published: 22 September 2016
eBook ISBN: 978-3-319-09259-1Published: 07 November 2014
Edition Number: 1
Number of Pages: X, 415
Number of Illustrations: 33 b/w illustrations, 45 illustrations in colour
Topics: Communications Engineering, Networks, Information Systems and Communication Service, Signal, Image and Speech Processing