Partitional Clustering Algorithms
Published on 20. November 2014
Book
Hardback
X, 415 pages
978-3-319-09258-4 (ISBN)
Description
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.
Reviews / Votes
"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)
More details
Edition
2015 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
33 s/w Abbildungen, 45 farbige Abbildungen
X, 415 p. 78 illus., 45 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 29 mm
Weight
805 gr
ISBN-13
978-3-319-09258-4 (9783319092584)
DOI
10.1007/978-3-319-09259-1
Schweitzer Classification
Other editions
Additional editions
M. Emre Celebi
Partitional Clustering Algorithms
Book
09/2016
Springer
€106.99
Shipment within 10-15 days
M. Emre Celebi
Partitional Clustering Algorithms
E-Book
11/2014
1st Edition
Springer
€96.29
Available for download
Person
Dr. Emre Celebi is an Associate Professor with the Department of Computer Science, at Louisiana State University in Shreveport.
Content
Recent developments in model-based clustering with applications.- Accelerating Lloyd's algorithm for k-means clustering.- Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm.- Nonsmooth optimization based algorithms in cluster analysis.- Fuzzy Clustering Algorithms and Validity Indices for Distributed Data.- Density Based Clustering: Alternatives to DBSCAN.- Nonnegative matrix factorization for interactive topic modeling and document clustering.- Overview of overlapping partitional clustering methods.- On Semi-Supervised Clustering.- Consensus of Clusterings based on High-order Dissimilarities.- Hubness-Based Clustering of High-Dimensional Data.- Clustering for Monitoring Distributed Data Streams.