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Computer Science - Database Management & Information Retrieval | Mathematical Tools for Data Mining - Set Theory, Partial Orders, Combinatorics

Mathematical Tools for Data Mining

Set Theory, Partial Orders, Combinatorics

Simovici, Dan A., Djeraba, Chabane

2nd ed. 2014, XI, 831 p. 93 illus.

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  • Focuses on mathematical topics of immediate interest to data mining and machine learning
  • The mathematics is illustrated by significant applications ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc
  • Includes more than 700 exercises and solutions

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.

Content Level » Research

Related subjects » Computational Science & Engineering - Database Management & Information Retrieval - Theoretical Computer Science

Table of contents / Preface / Sample pages 

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Distribution rights for India: Researchco Book Centre, New Delhi, India

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