Mixture Models and Applications

  • Nizar Bouguila
  • Wentao Fan

Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Gaussian-Based Models

  3. Generalized Gaussian-Based Models

    1. Front Matter
      Pages 59-59
    2. Muhammad Azam, Basim Alghabashi, Nizar Bouguila
      Pages 61-80
    3. Fatma Najar, Sami Bourouis, Rula Al-Azawi, Ali Al-Badi
      Pages 81-106
  4. Spherical and Count Data Clustering

  5. Bounded and Semi-bounded Data Clustering

    1. Front Matter
      Pages 177-177
    2. Kamal Maanicshah, Muhammad Azam, Hieu Nguyen, Nizar Bouguila, Wentao Fan
      Pages 209-233
    3. Meeta Kalra, Michael Osadebey, Nizar Bouguila, Marius Pedersen, Wentao Fan
      Pages 235-269
  6. Image Modeling and Segmentation

    1. Front Matter
      Pages 271-271
    2. Jaspreet Singh Kalsi, Muhammad Azam, Nizar Bouguila
      Pages 273-305
    3. Wenmin Chen, Wentao Fan, Nizar Bouguila, Bineng Zhong
      Pages 307-324
    4. Ines Channoufi, Fatma Najar, Sami Bourouis, Muhammad Azam, Alrence S. Halibas, Roobaea Alroobaea et al.
      Pages 325-348
  7. Back Matter
    Pages 349-355

About this book


This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature.

  • Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection;
  • Present theoretical and practical developments in mixture-based modeling and their importance in different applications;
  • Discusses perspectives and challenging future works related to mixture modeling.


Finite mixture models Infinite mixture models Bayesian/variational learning Nonparametric Bayesian approaches Subspace mixture models Outliers detection High-dimensional data Deep mixture models Unsupervised learning Semi-supervised learning

Editors and affiliations

  • Nizar Bouguila
    • 1
  • Wentao Fan
    • 2
  1. 1.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada
  2. 2.Department of Computer Science and TechnologyHuaqiao UniversityXiamenChina

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-030-23875-9
  • Online ISBN 978-3-030-23876-6
  • Series Print ISSN 2522-848X
  • Series Online ISSN 2522-8498
  • Buy this book on publisher's site