Skip to main content
SpringerLink
Log in

Partitional Clustering Algorithms

  • Book
  • © 2015

Overview

Editors:
  1. M. Emre Celebi

    1. Computer Science dept., Louisiana State University Shreveport, Shreveport, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • 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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (12 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Recent Developments in Model-Based Clustering with Applications

    • Volodymyr Melnykov, Semhar Michael, Igor Melnykov
    Pages 1-39

  3. Accelerating Lloyd’s Algorithm for k-Means Clustering

    • Greg Hamerly, Jonathan Drake
    Pages 41-78

  4. Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

    • M. Emre Celebi, Hassan A. Kingravi
    Pages 79-98

  5. Nonsmooth Optimization Based Algorithms in Cluster Analysis

    • Adil M. Bagirov, Ehsan Mohebi
    Pages 99-146

  6. Fuzzy Clustering Algorithms and Validity Indices for Distributed Data

    • L. Vendramin, M. C. Naldi, R. J. G. B. Campello
    Pages 147-192

  7. Density Based Clustering: Alternatives to DBSCAN

    • Christian Braune, Stephan Besecke, Rudolf Kruse
    Pages 193-213

  8. Nonnegative Matrix Factorization for Interactive Topic Modeling and Document Clustering

    • Da Kuang, Jaegul Choo, Haesun Park
    Pages 215-243

  9. Overview of Overlapping Partitional Clustering Methods

    • Chiheb-Eddine Ben N’Cir, Guillaume Cleuziou, Nadia Essoussi
    Pages 245-275

  10. On Semi-Supervised Clustering

    • Marco Bongini, Friedhelm Schwenker, Edmondo Trentin
    Pages 277-311

  11. Consensus of Clusterings Based on High-Order Dissimilarities

    • Helena Aidos, Ana Fred
    Pages 313-351

  12. Hubness-Based Clustering of High-Dimensional Data

    • Nenad Tomašev, Miloš Radovanović, Dunja Mladenić, Mirjana Ivanović
    Pages 353-386

  13. Clustering for Monitoring Distributed Data Streams

    • Maria Barouti, Daniel Keren, Jacob Kogan, Yaakov Malinovsky
    Pages 387-415
Back to top

Keywords

About this book

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

“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

  • Computer Science dept., Louisiana State University Shreveport, Shreveport, USA

    M. Emre Celebi

About the editor

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

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation