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

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...

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Bibliographic Details
Main Authors: Simovici, Dan A., Djeraba, Chabane (Author)
Format: eBook
Language:English
Published: London Springer London 2014, 2014
Edition:2nd ed. 2014
Series:Advanced Information and Knowledge Processing
Subjects:
Mathematics Of Computing
Computer Science / Mathematics
Discrete Mathematics In Computer Science
Computational Mathematics And Numerical Analysis
Mathematics / Data Processing
Data Mining
Data Mining And Knowledge Discovery
Discrete Mathematics
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Sets, Relations and Functions
  • Partially Ordered Sets
  • Combinatorics
  • Topologies and Measures
  • Linear Spaces
  • Norms and Inner Products
  • Spectral Properties of Matrices
  • Metric Spaces Topologies and Measures
  • Convex Sets and Convex Functions
  • Graphs and Matrices
  • Lattices and Boolean Algebras
  • Applications to Databases and Data Mining
  • Frequent Item Sets and Association Rules
  • Special Metrics
  • Dimensions of Metric Spaces
  • Clustering

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