Granular-Relational Data Mining

Piotr Honko

ISBN 10: 3319527509 / ISBN 13: 9783319527505

Editore: Berlin Springer International Publishing Springer Feb 2017, 2017

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 123 pp. Englisch.

Codice articolo 9783319527505

Dati bibliografici

• Titolo: Granular-Relational Data Mining
• Editore: Berlin Springer International Publishing Springer Feb 2017
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 3319527509
• ISBN 13: 9783319527505
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 242 L x 159 A x 12 P (millimetri) 242 millimetri di larghezza per 159 millimetri di altezza per 12 millimetri di profondità
• Peso: 362 grammi

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Riassunto

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.