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Granular-Relational Data Mining

How to Mine Relational Data in the Paradigm of Granular Computing?

  • Book
  • © 2017

Overview

Authors:
  1. Piotr Hońko

    1. Bialystok University of Technology, Faculty of Computer Science Bialystok University of Technology, Białystok, Poland

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  • Highlights research on mining relational data in the context of granular computing
  • Provides unified frameworks for performing typical data mining tasks such as classification, clustering, and association discovery
  • A unique fundamental text at the crossroads of two fields: relational data mining and granular computing
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 702)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Piotr Hońko
    Pages 1-6

  3. Generalized Related Set Based Approach

    1. Front Matter

      Pages 7-7
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    2. Information System for Relational Data

      • Piotr Hońko
      Pages 9-19

    3. Properties of Granular-Relational Data Mining Framework

      • Piotr Hońko
      Pages 21-28

    4. Association Discovery and Classification Rule Mining

      • Piotr Hońko
      Pages 29-38

    5. Rough-Granular Computing

      • Piotr Hońko
      Pages 39-48

  4. Description Language Based Approach

    1. Front Matter

      Pages 49-49
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    2. Compound Information Systems

      • Piotr Hońko
      Pages 51-63

    3. From Granular-Data Mining Framework to Its Relational Version

      • Piotr Hońko
      Pages 65-81

    4. Relation-Based Granules

      • Piotr Hońko
      Pages 83-97

    5. Compound Approximation Spaces

      • Piotr Hońko
      Pages 99-114

    6. Conclusions

      • Piotr Hońko
      Pages 115-116

  5. Back Matter

    Pages 117-123
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Keywords

About this book

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case.


Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing!


This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.

Authors and Affiliations

  • Bialystok University of Technology, Faculty of Computer Science Bialystok University of Technology, Białystok, Poland

    Piotr Hońko

Bibliographic Information

  • Book Title: Granular-Relational Data Mining

  • Book Subtitle: How to Mine Relational Data in the Paradigm of Granular Computing?

  • Authors: Piotr Hońko

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-52751-2

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG 2017

  • Hardcover ISBN: 978-3-319-52750-5Published: 10 February 2017

  • Softcover ISBN: 978-3-319-84977-5Published: 04 May 2018

  • eBook ISBN: 978-3-319-52751-2Published: 03 February 2017

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XV, 123

  • Number of Illustrations: 4 b/w illustrations

  • Topics: Computational Intelligence, Artificial Intelligence

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