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Supervised and Unsupervised Learning for Data Science

  • Michael W. Berry
  • Azlinah Mohamed
  • Bee Wah Yap
Book

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

Table of contents

  1. Front Matter
    Pages i-viii
  2. Algorithms

  3. Applications

    1. Front Matter
      Pages 83-83
    2. Dedy Dwi Prastyo, Halwa Annisa Khoiri, Santi Wulan Purnami, Suhartono, Soo-Fen Fam, Novri Suhermi
      Pages 85-100
    3. Dian I. Martin, Michael W. Berry, John C. Martin
      Pages 101-120
    4. Arisara Pornwattanavichai, Prawpan Brahmasakha na sakolnagara, Pongsakorn Jirachanchaisiri, Janekhwan Kitsupapaisan, Saranya Maneeroj
      Pages 121-143
    5. Ameer A. Jebur, Dhiya Al-Jumeily, Khalid R. Aljanabi, Rafid M. Al Khaddar, William Atherton, Zeinab I. Alattar et al.
      Pages 145-182
  4. Back Matter
    Pages 183-187

About this book

Introduction

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018).

  • Includes new advances in clustering and classification using semi-supervised and unsupervised learning;
  • Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning;
  • Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

Keywords

Semi-supervised and unsupervised learning Feature extraction and selection Discretization Learning Detection Learning algorithm design

Editors and affiliations

  • Michael W. Berry
    • 1
  • Azlinah Mohamed
    • 2
  • Bee Wah Yap
    • 3
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of Tennessee at KnoxvilleKnoxvilleUSA
  2. 2.Faculty of Computer & Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia
  3. 3.Advanced Analytics Engineering Centre, Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-22475-2
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-030-22474-5
  • Online ISBN 978-3-030-22475-2
  • Series Print ISSN 2522-848X
  • Series Online ISSN 2522-8498
  • Buy this book on publisher's site