Authors:
- 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)
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Front Matter
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Generalized Related Set Based Approach
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Front Matter
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Description Language Based Approach
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Front Matter
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Back Matter
About this book
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
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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