Mathematical Tools for Data Mining

• 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.

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)