Knowledge mining using intelligent agents [[electronic resource] /] / editors, Satchidananda Dehuri, Sung-Bae Cho

• Record Nr.: UNINA9910461625603321
• Pubbl/distr/stampa: London, : Imperial College Press, 2011
• ISBN: 1-283-14333-X; 9786613143334; 1-84816-387-8
• Descrizione fisica: 1 online resource (400 p.)
• Collana: Advances in computer science and engineering: Texts ; ; v. 6
• Altri autori (Persone): DehuriSatchidananda; ChoSung-Bae
• Disciplina: 006.312
• Soggetti: Intelligent agents (Computer software); Data mining; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: PREFACE; CONTENTS; Chapter 1THEORETICAL FOUNDATIONS OF KNOWLEDGE MINING AND INTELLIGENT AGENT; 1.1. Knowledge and Agent; 1.2. Knowledge Mining from Databases; 1.2.1. KMD tasks; 1.2.1.1. Mining Association Rules; 1.2.1.2. Classification; 1.2.1.3. Clustering; 1.2.1.4. Dependency Modeling; 1.2.1.5. Change and Deviation Detection; 1.2.1.6. Regression; 1.2.1.7. Summarization; 1.2.1.8. Causation Modeling; 1.3. Intelligent Agents; 1.3.1. Evolutionary computing; 1.3.2. Swarm intelligence; 1.3.2.1. Particle Swarm Optimization; 1.3.2.2. Ant Colony Optimization (ACO); 1.3.2.3. Artificial Bee Colony (ABC)1.3.2.4. Artificial Wasp Colony (AWC); 1.3.2.5. Artificial Termite Colony (ATC); 1.4. Summary; References; Chapter 2 THE USE OF EVOLUTIONARY COMPUTATION IN KNOWLEDGE DISCOVERY: THE EXAMPLE OF INTRUSION DETECTION SYSTEMS; 2.1. Introduction; 2.2. Background; 2.2.1. Knowledge discovery and data mining; 2.2.2. Evolutionary computation; 2.2.3. Intrusion detection systems; 2.3. The Role of Evolutionary Computation in KDD; 2.3.1. Feature selection; 2.3.2. Classification; 2.3.2.1. Representation; 2.3.2.2. Learning approaches; 2.3.2.3. Rule discovery; 2.3.3. Regression2.3.4. Clustering; 2.3.5. Comparison between classification and regression; 2.4. Evolutionary Operators and Niching; 2.4.1. Evolutionary operators; 2.4.2. Niching; 2.5. Fitness Function; 2.6. Conclusions and Future Directions; Acknowledgment; References; Chapter 3 EVOLUTION OF NEURAL NETWORK AND POLYNOMIAL NETWORK; 3.1. Introduction; 3.2. Evolving Neural Network; 3.2.1. The evolution of connection weights; 3.2.2. The evolution of architecture; 3.2.3. The evolution of node transfer function; 3.2.4. Evolution of learning rules; 3.2.5. Evolution of algorithmic parameters; 3.3. Evolving Neural Network using Swarm Intelligence3.3.1. Particle swarm optimization; 3.3.2. Swarm intelligence for evolution of neural network architecture; 3.3.2.1. Particle representation; 3.3.2.2. Fitness evaluation; 3.3.3. Simulation and results; 3.4. Evolving Polynomial Network (EPN) using Swarm Intelligence; 3.4.1. GMDH-type polynomial neural network model; 3.4.2. Evolving polynomial network (EPN) using PSO; 3.4.3. Parameters of evolving polynomial network (EPN); 3.4.3.1. Highest degree of the polynomials; 3.4.3.2. Number of terms in the polynomials; 3.4.3.3. Maximum unique features in each term of the polynomials3.4.4. Experimental studies for EPN; 3.5. Summary and Conclusions; References; Chapter 4 DESIGN OF ALLOY STEELS USING MULTI-OBJECTIVE OPTIMIZATION; 4.1. Introduction; 4.2. The Alloy Optimal Design Problem; 4.3. Neurofuzzy Modeling for Mechanical Property Prediction; 4.3.1. General scheme of neurofuzzy models; 4.3.2. Incorporating knowledge into neurofuzzy models; 4.3.3. Property prediction of alloy steels using neurofuzzy models; 4.3.3.1. Tensile strength prediction for heat-treated alloy steels; 4.3.3.2. Impact toughness prediction for heat-treated alloy steels
• Sommario/riassunto: ""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