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 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 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.
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.