Mathematical Tools for Data Mining : Set Theory, Partial Orders, Combinatorics
Language: English
Published by Springer, 2016
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 171.14
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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.
Seller Inventory # 9781447171348
- Title
- Mathematical Tools for Data Mining : Set Theory, Partial Orders, Combinatorics
- Author
- Chabane Djeraba
- Publisher
- Springer
- Publication year
- 2016
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1447171349
- ISBN 13
- 9781447171348
- Item weight
- 1,253 grams
- Dimensions
- 235x155x45 mm
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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