Granular-Relational Data Mining

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

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Granular-Relational Data Mining by Piotr Hońko, Springer International Publishing
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Author: Piotr Hońko ISBN: 9783319527512
Publisher: Springer International Publishing Publication: February 3, 2017
Imprint: Springer Language: English
Author: Piotr Hońko
ISBN: 9783319527512
Publisher: Springer International Publishing
Publication: February 3, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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