The K-means algorithm is commonly used in data mining and business intelligence. This award-winning research pioneers its application to the intricacies of ‘big data’, detailing a theoretical framework for aggregating and validating clusters with K-means.
This volume synthesises research and development in cognitive science and computing that deal with the automated assessment of human emotion. Affect computing systems are used in identifying terror threats, and are extensively used in marketing and finance. This volume comprises affect computing systems that learn to identify sentiment bearing sentences, and help in evaluating the polarity of opinion, positive/negative, in written text and speech.
This unique text seeks to automate the design of a data mining algorithm. It first overviews data mining and evolutionary algorithms then discusses the design of a new genetic programming system for automating the design of full rule induction algorithms.
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.
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 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 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 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.
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.