<P>The maturing of the field of data mining has brought about an increased level of mathematical sophistication. Such disciplines like topology, combinatorics, partially ordered sets and their associated algebraic structures (lattices and Boolean algebras), and metric spaces are increasingly applied
Mathematical tools for data mining: set theory, partial orders, combinatorics
β Scribed by Dan A. Simovici, Chabane Djeraba (auth.)
- Publisher
- Springer London
- Year
- 2008
- Tongue
- English
- Leaves
- 610
- Series
- Advanced information and knowledge processing
- Edition
- 1st Edition.
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Subjects
Data Mining and Knowledge Discovery; Mathematics of Computing; Discrete Mathematics in Computer Science; Computational Mathematics and Numerical Analysis
π SIMILAR VOLUMES
<p>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 a
<p>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 a
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 appl