Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics
Author:   Dan A. Simovici ,  Chabane Djeraba
Publisher:   Springer London Ltd
Edition:   2008 ed.
ISBN:  

9781848002005


Pages:   628
Publication Date:   15 August 2008
Format:   Hardback
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics


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Author:   Dan A. Simovici ,  Chabane Djeraba
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   2008 ed.
Dimensions:   Width: 16.00cm , Height: 3.40cm , Length: 24.00cm
Weight:   1.056kg
ISBN:  

9781848002005


ISBN 10:   1848002009
Pages:   628
Publication Date:   15 August 2008
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Set Theory.- Sets, Relations, and Functions.- Algebras.- Graphs and Hypergraphs.- Partial Orders.- Partially Ordered Sets.- Lattices and Boolean Algebras.- Topologies and Measures.- Frequent Item Sets and Association Rules.- Applications to Databases and Data Mining.- Rough Sets.- Metric Spaces.- Dissimilarities, Metrics, and Ultrametrics.- Topologies and Measures on Metric Spaces.- Dimensions of Metric Spaces.- Clustering.- Combinatorics.- Combinatorics.- The Vapnik-Chervonenkis Dimension.

Reviews

From the reviews: The book is organized into four parts, with a total of 15 chapters. Each chapter ! offers numerous exercises and references for further reading. ! Overall, Simovici and Djeraba's presentation of both the theoretical grounds and the practical aspects of the various data mining methodologies is good. ! The book is intended for readers who have a data mining background ! . It will help this audience to improve their knowledge of how different data mining strategies operate from a mathematical standpoint. (Aris Gkoulalas-Divanis, ACM Computing Reviews, February, 2009)


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