Partitional Clustering Algorithms
1st Edition
Published on 7. November 2014
X, 415 pages
978-3-319-09259-1 (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
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
X, 415 p. 78 illus., 45 illus. in color.
File size
8,62 MB
ISBN-13
978-3-319-09259-1 (9783319092591)
DOI
10.1007/978-3-319-09259-1
Schweitzer Classification
Other editions
Additional editions
M. Emre Celebi
Partitional Clustering Algorithms
Book
11/2014
Springer
€106.99
Shipment within 10-15 days
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.
