LEADER 05539nam 2200685 a 450 001 9910461625603321 005 20200520144314.0 010 $a1-283-14333-X 010 $a9786613143334 010 $a1-84816-387-8 035 $a(CKB)2670000000095729 035 $a(EBL)731136 035 $a(OCoLC)738434230 035 $a(SSID)ssj0000523739 035 $a(PQKBManifestationID)12210091 035 $a(PQKBTitleCode)TC0000523739 035 $a(PQKBWorkID)10542913 035 $a(PQKB)11660269 035 $a(MiAaPQ)EBC731136 035 $a(WSP)0000P639 035 $a(Au-PeEL)EBL731136 035 $a(CaPaEBR)ebr10480131 035 $a(CaONFJC)MIL314333 035 $a(EXLCZ)992670000000095729 100 $a20110714d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aKnowledge mining using intelligent agents$b[electronic resource] /$feditors, Satchidananda Dehuri, Sung-Bae Cho 210 $aLondon $cImperial College Press$d2011 215 $a1 online resource (400 p.) 225 1 $aAdvances in computer science and engineering: Texts ;$vv. 6 300 $aDescription based upon print version of record. 311 $a1-84816-386-X 320 $aIncludes bibliographical references. 327 $aPREFACE; 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) 327 $a1.3.2.3. 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 327 $a2.3.3. 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 327 $a3.3. 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 327 $a3.4.3.3. 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 327 $a4.3.3.2. Impact toughness prediction for heat-treated alloy steels 330 $a""Knowledge Mining Using Intelligent Agents"" explores the concept of knowledge discovery processes and enhances decision-making capability through the use of intelligent agents like ants, termites and honey bees. In order to provide readers with an integrated set of concepts and techniques for understanding knowledge discovery and its practical utility, this book blends two distinct disciplines - data mining and knowledge discovery process, and intelligent agents-based computing (swarm intelligence and computational intelligence). For the more advanced reader, researchers, and decision/policy 410 0$aAdvances in computer science and engineering.$pTexts ;$vv. 6. 606 $aIntelligent agents (Computer software) 606 $aData mining 608 $aElectronic books. 615 0$aIntelligent agents (Computer software) 615 0$aData mining. 676 $a006.312 701 $aDehuri$b Satchidananda$0889069 701 $aCho$b Sung-Bae$0880444 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910461625603321 996 $aKnowledge mining using intelligent agents$91986524 997 $aUNINA