This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. It is intended for a professional audience, but is also appropriate for advanced-level students in computer science.
This volume focuses on a major machine learning task known as classification. Some classification problems are hard to solve, but this book shows that they can be decomposed into much simpler sub-problems.
Multi-database mining has been recognized as a strategically essential area of research in data mining. This book discusses various issues regarding the systematic and efficient development of multi-database mining applications.
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.
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 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.
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 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.
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.