Offering the reader a reference to the mathematical tools required for data mining, this book integrates the mathematics of data mining with its applications. It provides the necessary mathematical background for researchers and graduate students.
This collection of surveys presents the most recent models, algorithms and applications in mining uncertain data. Organized to make it more accessible to applications-driven practitioners, it includes case studies based on real-world examples.
This is the first coherent book on literature-based discovery (LBD). LBD is an inherently multi-disciplinary enterprise. The aim of this volume is to plant a flag in the ground and inspire new researchers to the LBD challenge.
This study of the theory of generalizations of rough-set models in incomplete information systems discusses not only the regular attributes but also the criteria in these systems, and presents practical approaches to computing a number of reducts.
Adopting data geometry as a framework to address dimensionality reduction, this volume introduces well known linear methods, stressing recently developed ones, and covers various dimensionality reduction applications including hyperspectral imagery.
This is an introductory textbook and guide to the rapidly evolving field of predictive text mining. There are chapter summaries, historical and bibliographic remarks, and classroom-tested exercises for each chapter. Descriptive case studies are also included.
This book reviews the basics of rule learning as applied to classical machine learning and modern data mining. It connects attribute-value learning with inductive logic programming, and offers complete coverage of most important elements of rule learning.
This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.