Authors:
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (16 chapters)
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Front Matter
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Back Matter
About this book
Reviews
From the book reviews:
“This textbook is appropriate for an advanced undergraduate or graduate mathematics elective class. All theorems are proved, notation is standard, and ample exercise sets are included at the end of every chapter. … Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics is more than just the data-mining reference book. It is highly readable textbook that successfully connects classic, theoretical mathematics to an enormously popular current application in modern society.” (Susan D’Agostino, MAA Reviews, March, 2015)
“The goal of this book is to present the basic mathematical theory and principles used in data mining tools and techniques. … Graduate or advanced undergraduate students with prior coursework in mathematics will find this book a useful collection of the fundamental mathematical ideas … . The exposition of concepts is clear and readable. Comfort with mathematical notation is necessary, since the book makes significant use of such notation. Several exercises are included, with solutions being provided in outline.” (R. M. Malyankar, Computing Reviews, September, 2014)Authors and Affiliations
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University of Massachusetts, Boston Dept. Computer Science, Boston, USA
Dan A. Simovici
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University Lille 1, Laboratoire d'Informatique Fundamentale de Lille, Villeneuve d'Ascq, France
Chabane Djeraba
Bibliographic Information
Book Title: Mathematical Tools for Data Mining
Book Subtitle: Set Theory, Partial Orders, Combinatorics
Authors: Dan A. Simovici, Chabane Djeraba
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/978-1-4471-6407-4
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2014
Hardcover ISBN: 978-1-4471-6406-7Published: 09 April 2014
Softcover ISBN: 978-1-4471-7134-8Published: 03 September 2016
eBook ISBN: 978-1-4471-6407-4Published: 27 March 2014
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 2
Number of Pages: XI, 831
Number of Illustrations: 93 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Mathematics of Computing, Discrete Mathematics in Computer Science, Computational Mathematics and Numerical Analysis