Granular-relational data mining : how to mine relational data in the paradigm of granular computing?

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 m...

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Bibliographic Details
Main Author: Hońko, Piotr, (Author)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2017.
Series: Studies in computational intelligence ; v. 702.
Subjects:
Data mining.
Granular computing.
elektronické knihy
electronic books
ISBN: 9783319527512
9783319849775
9783319527505
Physical Description: 1 online resource (xv, 123 pages) : illustrations

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100 1 |a Hońko, Piotr,  |e author. 
245 1 0 |a Granular-relational data mining :  |b how to mine relational data in the paradigm of granular computing? /  |c Piotr Hońko. 
264 1 |a Cham, Switzerland :  |b Springer,  |c 2017. 
300 |a 1 online resource (xv, 123 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Studies in computational intelligence,  |x 1860-949X ;  |v volume 702 
504 |a Includes bibliographical references and index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
505 0 |a Preface -- Chapter 1: Introduction -- Part I: Generalized Related Set Based Approach -- Chapter 2: Information System for Relational Data -- Chapter 3: Properties of Granular-Relational Data Mining Framework -- Chapter 4: Association Discovery and Classification Rule Mining -- Chapter 5: Rough-Granular Computing -- Part II: Description Language Based Approach -- Chapter 6: Compound Information Systems -- Chapter 7: From Granular-Data Mining Framework to its Relational Version -- Chapter 8: Relation-Based Granules -- Chapter 9: Compound Approximation Spaces -- Conclusions -- References -- Index. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Data mining. 
650 0 |a Granular computing. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |a Hońko, Piotr.  |t Granular-relational data mining.  |d Cham, Switzerland : Springer, 2017  |z 3319527509  |z 9783319527505  |w (OCoLC)967373804 
830 0 |a Studies in computational intelligence ;  |v v. 702.  |x 1860-949X 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-52751-2  |y Plný text 
992 |c NTK-SpringerENG 
999 |c 99798  |d 99798 
993 |x NEPOSILAT  |y EIZ 

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