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  • © 2016

Unsupervised Learning Algorithms

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  • Contains the state-of-the-art in unsupervised learning in a single comprehensive volume

  • Features numerous step-by-step tutorials help the reader to learn quickly

  • Includes several tips on how to protect flash sites from hackers and a special chapter on next generation Flash that prepares readers for the future

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USD 89.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-24211-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 119.99
Price excludes VAT (USA)
Hardcover Book
USD 119.99
Price excludes VAT (USA)

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-x
  2. Anomaly Ranking in a High Dimensional Space: The Unsupervised TreeRank Algorithm

    • S. Clémençon, N. Baskiotis, N. Vayatis
    Pages 33-54
  3. Clustering Evaluation in High-Dimensional Data

    • Nenad Tomašev, Miloš Radovanović
    Pages 71-107
  4. Kernel Spectral Clustering and Applications

    • Rocco Langone, Raghvendra Mall, Carlos Alzate, Johan A. K. Suykens
    Pages 135-161
  5. Uni- and Multi-Dimensional Clustering Via Bayesian Networks

    • Omid Keivani, Jose M. Peña
    Pages 163-192
  6. A Survey of Constrained Clustering

    • Derya Dinler, Mustafa Kemal Tural
    Pages 207-235
  7. An Overview of the Use of Clustering for Data Privacy

    • Vicenç Torra, Guillermo Navarro-Arribas, Klara Stokes
    Pages 237-251
  8. Nonlinear Clustering: Methods and Applications

    • Chang-Dong Wang, Jian-Huang Lai
    Pages 253-302
  9. Swarm Intelligence-Based Clustering Algorithms: A Survey

    • Tülin İnkaya, Sinan Kayalıgil, Nur Evin Özdemirel
    Pages 303-341
  10. A Fuzzy-Soft Competitive Learning Approach for Grayscale Image Compression

    • Dimitrios M. Tsolakis, George E. Tsekouras
    Pages 385-404
  11. Unsupervised Learning in Genome Informatics

    • Ka-Chun Wong, Yue Li, Zhaolei Zhang
    Pages 405-448
  12. The Application of LSA to the Evaluation of Questionnaire Responses

    • Dian I. Martin, John C. Martin, Michael W. Berry
    Pages 449-484
  13. Mining Evolving Patterns in Dynamic Relational Networks

    • Rezwan Ahmed, George Karypis
    Pages 485-532
  14. Probabilistically Grounded Unsupervised Training of Neural Networks

    • Edmondo Trentin, Marco Bongini
    Pages 533-558

About this book

This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.

Keywords

  • Big Data Patterns
  • Data Analytics
  • Data Mining
  • Elements Statistical Learning
  • Genomic Data Sets
  • Machine Learning
  • Pattern Recognition
  • Statistical Learning Theory
  • Unsupervised Algorithms
  • Unsupervised Learning

Reviews

“The book provides a valuable survey of an area of both research and application, particularly as massive datasets have become available. … The book can be recommended to anyone interested in getting an overview of this fast-moving research and application area. Because each chapter has a comprehensive bibliography, the book can serve as an entry point for those wishing to work in or with unsupervised learning.” (J. P. E. Hodgson, Computing Reviews, computingreviews.com, August, 2016)

Editors and Affiliations

  • Computer Science, Louisiana State University in Shreveport, Shreveport, USA

    M. Emre Celebi

  • North American University, Houston, USA

    Kemal Aydin

Bibliographic Information

Buying options

eBook
USD 89.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-24211-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 119.99
Price excludes VAT (USA)
Hardcover Book
USD 119.99
Price excludes VAT (USA)