Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics (Advanced Information and Knowledge Processing)

Simovici, Dan A.; Djeraba, Chabane

ISBN 10: 1447171349 ISBN 13: 9781447171348

Editorial: Springer, 2016

Idioma: Ingl�s

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Print on Demand pp. 831.

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Detalles bibliogr�ficos

T�tulo

Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics (Advanced Information and Knowledge Processing)

Editor

Springer

Fecha de publicaci�n

2016

Idioma

Ingl�s

ISBN 10

1447171349

ISBN 13

9781447171348

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

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Sinopsis

Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. Topics include partially ordered sets, combinatorics, general topology, metric spaces, linear spaces, graph theory. To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc. The book is intended as a reference for researchers and graduate students. The current edition is a significant expansion of the first edition. We strived to make the book self-contained and only a general knowledge of mathematics is required. More than 700 exercises are included and they form an integral part of the material. Many exercises are in reality supplemental material and their solutions are included.

De la contraportada

The current edition is a significant expansion of the first edition. We strived to make the book self-contained, and only a general knowledge of mathematics is required. More than 700 exercises are included and they form an integral part of the material. Many exercises are in reality supplemental material and their solutions are included.

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

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