Partitional Clustering Algorithms

Author:   M. Emre Celebi
Publisher:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2015
ISBN:  

9783319347981


Pages:   415
Publication Date:   22 September 2016
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Partitional Clustering Algorithms


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Overview

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.

Full Product Details

Author:   M. Emre Celebi
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2015
Dimensions:   Width: 15.50cm , Height: 2.20cm , Length: 23.50cm
Weight:   6.438kg
ISBN:  

9783319347981


ISBN 10:   3319347985
Pages:   415
Publication Date:   22 September 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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)


Author Information

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

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