Mathematical Tools for Data Mining (Hardcover)

Dan A. Simovici

ISBN 10: 1447164067 / ISBN 13: 9781447164067

Published by Springer London Ltd, England, 2014

About this Item

Hardcover. 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. Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781447164067

Bibliographic Details

• Title: Mathematical Tools for Data Mining (Hardcover)
• Publisher: Springer London Ltd, England
• Publication Date: 2014
• Language: English
• ISBN 10: 1447164067
• ISBN 13: 9781447164067
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: new

About this title

Synopsis

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.