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Redescription Mining

  • Esther Galbrun
  • Pauli Miettinen

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Esther Galbrun, Pauli Miettinen
    Pages 1-23
  3. Esther Galbrun, Pauli Miettinen
    Pages 25-49
  4. Esther Galbrun, Pauli Miettinen
    Pages 51-80

About this book

Introduction

This book provides a gentle introduction to redescription mining, a versatile data mining tool that is useful to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. It is intended for readers who are familiar with basic data analysis techniques such as clustering, frequent itemset mining, and classification. Redescription mining is defined in a general way, making it applicable to different types of data. The general framework is made more concrete through many practical examples that show the versatility of redescription mining. The book also introduces the main algorithmic ideas for mining redescriptions, together with applications from various domains. The final part of the book contains variations and extensions of the basic redescription mining problem, and discusses some future directions and open questions. 

Keywords

Redescription mining Alternative characterizations Data mining Multi-view data analysis Interpretable patterns Visualizations

Authors and affiliations

  • Esther Galbrun
    • 1
  • Pauli Miettinen
    • 2
  1. 1.Inria Nancy – Grand EstVillers-lès-NancyFrance
  2. 2.Max-Planck-Institute for InformaticsSaarbrückenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-72889-6
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-72888-9
  • Online ISBN 978-3-319-72889-6
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site
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