Unsupervised Learning Algorithms

  • M. Emre Celebi
  • Kemal Aydin

Table of contents

  1. Front Matter
    Pages i-x
  2. S. Clémençon, N. Baskiotis, N. Vayatis
    Pages 33-54
  3. Nenad Tomašev, Miloš Radovanović
    Pages 71-107
  4. Rocco Langone, Raghvendra Mall, Carlos Alzate, Johan A. K. Suykens
    Pages 135-161
  5. Omid Keivani, Jose M. Peña
    Pages 163-192
  6. Derya Dinler, Mustafa Kemal Tural
    Pages 207-235
  7. Vicenç Torra, Guillermo Navarro-Arribas, Klara Stokes
    Pages 237-251
  8. Chang-Dong Wang, Jian-Huang Lai
    Pages 253-302
  9. Tülin İnkaya, Sinan Kayalıgil, Nur Evin Özdemirel
    Pages 303-341
  10. Dimitrios M. Tsolakis, George E. Tsekouras
    Pages 385-404
  11. Ka-Chun Wong, Yue Li, Zhaolei Zhang
    Pages 405-448
  12. Dian I. Martin, John C. Martin, Michael W. Berry
    Pages 449-484
  13. Rezwan Ahmed, George Karypis
    Pages 485-532
  14. 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.


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

Editors and affiliations

  • M. Emre Celebi
    • 1
  • Kemal Aydin
    • 2
  1. 1.Computer ScienceLouisiana State University in ShreveportShreveportUSA
  2. 2.North American UniversityHoustonUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-24209-5
  • Online ISBN 978-3-319-24211-8
  • Buy this book on publisher's site