Knowledge mining using intelligent agents (Imperial College Pr., 2011)
Includes bibliographical references
1. Theoretical foundations of knowledge mining and intelligent agent / S. Dehuri and S.-B. Cho -- 2. The use of evolutionary computation in knowledge discovery : The example of intrusion detection systems / S.X. Wu and W. Banzhaf -- 3. Evolution of neural network and polynomial network / B.B. Misra, P.K. Dash and G. Panda -- 4. Design of alloy steels using multi-objective optimization / M. Chen, V. Kadirkamanathan and P.J. Fleming -- 5. An extended Bayesian/HAPSO intelligent method in intrusion detection system / S. Dehuri and S. Tripathy -- 6. Mining knowledge from network intrusion data using data mining techniques / M. Panda and M.R. Patra -- 7. Particle swarm optimization for multi-objective optimal operational planning of energy plants / Y. Fukuyama, H. Nishida and Y. Todaka -- 8. Soft computing for feature selection / A.K. Jagadev, S. Devi and R. Mall -- 9. Optimized polynomial fuzzy swarm net for classification / B.B. Misra, P.K. Dash and G. Panda -- 10. Software testing using genetic algorithms / M. Ray and D.P. Mohapatra
Knowledge Mining Using Intelligent Agents explores the concept of knowledge discovery processes and enhances decision-making capability through the use of intelligent agents like ants, termites and honey bees. In order to provide readers with an integrated set of concepts and techniques for understanding knowledge discovery and its practical utility, this book blends two distinct disciplines - data mining and knowledge discovery process, and intelligent agents-based computing (swarm intelligence and computational intelligence). For the more advanced reader, researchers, and decision/policy-makers are given an insight into emerging technologies and their possible hybridization, which can be used for activities like dredging, capturing, distributions and the utilization of knowledge in their domain of interest (i.e. business, policy-making, etc.). By studying the behavior of swarm intelligence, this book aims to integrate the computational intelligence paradigm and intelligent distributed agents architecture to optimize various engineering problems and efficiently represent knowledge from the large gamut of data