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.

Focuses on mathematical topics of immediate interest to data mining and machine learning

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.