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 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.
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.
Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The book bridges the gap between business expectations and research outputs.
This book details the state-of-the-art research and development regarding the synergism of multiagents and data mining. Specifically, it presents the methodologies, algorithms and systems that integrate these two cutting-edge technologies.
This book provides a thorough introduction to the use of data mining algorithms as an investigative tool for applications in genomics. It then explores tremendous advances in the field and offers frontier case studies based on current research.
This clear and accessible introduction to the subject shows how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. It also covers subjects such as customer analysis in greater depth.
This comprehensive textbook on data mining details the unique steps of the knowledge discovery process - an industry standard that prescribes the sequence in which projects should be performed, from data understanding and preprocessing to deployment of the results.
This book describes the Dark Web landscape of international terrorism, suggests a systematic, computational approach to understanding its problems, and presents techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team.
This book collects current computational research that addresses critical issues for countering terrorism, including finding relevant information from large, changing data stores and producing actionable intelligence by finding meaningful patterns.