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| Bigus J.P. — Data mining with neural networks |
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| Ïðåäìåòíûé óêàçàòåëü |
Adaline model 27—28
Adaptive resonance theory (ART) 74—75 90 184
Agent communication language (ACL) 121
agents see intelligent agents
Algorithms, clustering 13
Algorithms, genetic 199—205
Algorithms, rule output from an association 13
Analysis 99—101 187—188
Analysis, output 126
Analysis, sensitivity 100—101 161—162
Application Programming Interface (API) 112 151 175 188
Applications, data mining 16—20
Applications, data mining, customer ranking model 155—165
Applications, data mining, decision support 99
Applications, data mining, energy and utility 20 129
Applications, data mining, finance 18—19
Applications, data mining, health and medical 20
Applications, data mining, manufacturing 19—20
Applications, data mining, market segmentation 131—142
Applications, data mining, marketing 17
Applications, data mining, real estate pricing model 143—153
Applications, data mining, retail 17—18
Applications, data mining, sales forecasting 167—177
Applications, developing business 15—16
Applications, neural network 29
Applications, neural network, developing 109—110
Applications, neural network, transaction processing 110—111
Arithmetic logic unit (ALU) 34
artificial intelligence 27—28
ARTMAP 75
Associative memory 39
Back propagation 69—71 78 184 203
Back propagation, recurrent 73 184
Binary codes 53—54
Boltzmann network 76
Brokering agents 120
Business applications 15—16
central processing unit (CPU) 34
Classification 38 83—84
Clustering 13 38—39 84—85 105—106 107 140 142
Commercial agents 120
Common object request broker (CORBA) 121
Computer systems, digital architecture 34—35
Computer systems, early IBM 4
Computer systems, mainframe data storage 5
continuous values 54—55
Crossover 201—202
Customer ranking application 155—165
Customer ranking application, data representation 156—158
Customer ranking application, deploying 163
Customer ranking application, maintaining 163
Customer ranking application, selecting a model 158
Customer ranking application, selecting architecture 158
Customer ranking application, selecting data 156
Customer ranking application, sensitivity analysis 161—162
Customer ranking application, training/testing 158—161
DATA 43—59
Data mining, adding learning to agents through 124—125
Data mining, agent-directed 126—127
Data mining, applications 16—20 99 129
Data mining, automating using intelligent agents 125—127
Data mining, decision support applications 99
Data mining, definition/description xiv 9
Data mining, functions 12
Data mining, overview 9—14
Data mining, process xiv 1—2 10
Data representation 52—55 93 134—135 145—146 156—158 169—170
Data representation, binary codes 53—54
Data representation, continuous values 54—55
Data representation, discrete values 53
Data representation, impact on training time 55—56
Data representation, numeric 52—53
Data representation, one-of N codes 53
Data representation, symbolic 55
Data representation, thermometer codes 54
Data warehousing 6—9
Data warehousing, architectural diagram of 8
Data, cleansing 48—49 125
Data, preparing 125
Data, preprocessing 49—51 125—126
Data, quality 57—58
Data, quantity 57
Data, raw material 43—44
Data, segmenting with neural networks 136—138
data, selecting 49 125 132—134 145 156 168—169
Databases, modern systems 44—46
Databases, parallel 46—48
databases, relational 45
Databases, shared-nothing architectures 47—48
Databases, SMP architectures 46—47
Decision support systems (DSS) 14—15
Delta rule 70
Discrete values 53
Domain knowledge 106 121—124
Encoding 200
Energy and utility applications 20
Executive information systems (EIS) 14
Expert systems, fuzzy 123—124
Expert systems, rule-based 127
Expert systems, traditional 122—123
Filtering agents 117—118
Financial applications 18—19
Forecasting 39—40 86—87
Forecasting, time-series 70—71 86
Fuzzy logic 123—124 191—198
Fuzzy logic, definition/description 198
Fuzzy logic, neural networks and 197
Fuzzy rules 186—187 195—197 198
Fuzzy sets 192—194
Fuzzy variables 195
Generalized delta rule 69
Generalized regression neural network (GRNN) 76
Genetic algorithms 199—205
Gradient descent 69
Graphics, Hinton diagram 105
Graphics, neural network 103—104
Graphics, standard 102—103
Health and medical applications 20
Hinton diagram 105
Hopfield network 76
Information agents 118
Intelligent agents, adding learning to through data mining 124—125
Intelligent agents, automating data mining using 125—127
Intelligent agents, brokering 120
Intelligent agents, classification 127
Intelligent agents, commercial 120
Intelligent agents, definition/description 115—116
Intelligent agents, information 118
Intelligent agents, multiagent systems 120—121
Intelligent agents, office/work flow 119
Intelligent agents, ratering 117—118
Intelligent agents, system 119—120
Intelligent agents, types of 116—121
Intelligent agents, user interface 118—119
Interactive development environment (IDE) 180—181
Internet 118
Knowledge interchange format (KIF) 121
Knowledge query and manipulation language (KQML) 121
Kohonen feature maps 71—73 78 90 184
Learning Vector Quantization (LVQ) 71
Linguistic variables 195
Management information system (MIS) 6
Manufacturing applications 19—20
Mapping, symbolic 50—51
| Market segmentation 131—142
Market segmentation, data representation 134—135
Market segmentation, segmenting data with neural networks 136—138
Market segmentation, selecting a model 135
Market segmentation, selecting architecture 135
Market segmentation, selecting data for 132—134
Marketing applications 17
Mean squared error (MSE) 85
Memory, associative 39
Modeling 39 85—86
Models and paradigms 68—76 183—185
Models and paradigms, adaptive resonance theory 74—75 90 184
Models and paradigms, architecture selection 126
Models and paradigms, architectures 93—94
Models and paradigms, automating the building process 94—95
Models and paradigms, back propagation 69—71 78 184 203
Models and paradigms, Boltzmann network 76
Models and paradigms, functions 77
Models and paradigms, generalized regression neural network 76
Models and paradigms, Hopfield network 76
Models and paradigms, Kohonen feature maps 71—73 78 90 184
Models and paradigms, learning 61—65
Models and paradigms, learning, reinforcement 64—65
Models and paradigms, learning, supervised 62—63
Models and paradigms, learning, unsupervised 63—64
Models and paradigms, probabilistic neural network 75—76
Models and paradigms, radial basis function 73—74 184—185
Models and paradigms, recurrent back propagation 73 184
Models and paradigms, selecting 76—77 92—93 146—147 158 170—171
Models and paradigms, temporal difference learning network 185
Mutation 202
networks and networking see models and paradigms; neural networks
Neural network utility (NNU) 136—137 140 148—150 159—160 164 175 179—190
Neural network utility (NNU), analysis 187—188
Neural network utility (NNU), application generation function 182—183
Neural network utility (NNU), data preparation 181—183
Neural network utility (NNU), deploying applications 188—189
Neural network utility (NNU), fuzzy rule systems 186—187
Neural network utility (NNU), inspectors 187—188
Neural network utility (NNU), interactive development environment 180—181
Neural network utility (NNU), maintaining applications 188—189
Neural network utility (NNU), models and architectures 183—185
Neural network utility (NNU), product overview 179—180
Neural network utility (NNU), scripting 185
Neural network utility (NNU), training/testing 185
Neural network utility (NNU), translate filter 181—182 189
Neural network utility (NNU), visualization 187—188
Neural networks 23—42
Neural networks, commercial applications 29
Neural networks, computer metaphor vs. brain metaphor 26—30
Neural networks, decision-making process 34—36
Neural networks, definition/description xiv
Neural networks, development process 91—94
Neural networks, development tools 29
Neural networks, functions 38—41
Neural networks, history 23—25
Neural networks, knowledge workers and 32—34
Neural networks, layers 78
Neural networks, learning parameters 82
Neural networks, learning process 37—38
Neural networks, maintaining 112—113
Neural networks, models see models and paradigms
Neural networks, performance 113
Neural networks, symbol processing vs. subsymbolic processing 25—26
Neural networks, topologies 65—68
Neural networks, training see training
Normalization 50
numeric data 52—53
object-oriented programming (OOP) 15
Office management agents 119
Online analytical processing (OLAP) 5
Operators, genetic 201—202
Parallel distributed processing (PDP) 28
Perceptron model 27—28
Prediction 39—40
Probabilistic neural network (PNN) 75—76
Programs and programming, object-oriented 15
Radial basis function (RBF) 73—74 184—185
Real estate application 143—153
Real estate application, data representation 145—146
Real estate application, deploying 151
Real estate application, maintaining 151
Real estate application, selecting a model 146—147
Real estate application, selecting architecture 146—147
Real estate application, selecting data 145
Real estate application, training/testing 147—151
Recurrent back propagation 73 184
Regression 39
Retail applications 17—18
Root mean squared (RMS) 85
Rule generation 101—102
Sales forecasting application 167—177
Sales forecasting application, data representation 169—170
Sales forecasting application, deploying 175—176
Sales forecasting application, maintaining 175—176
Sales forecasting application, selecting a model 170—171
Sales forecasting application, selecting architecture 170—171
Sales forecasting application, selecting data 168—169
Sales forecasting application, training/testing 171—175
Scaling 50
scripting 112 121 185
Segmentation 72 105—106 107 136—138 140 142
Segmentation market 131—142
Sensitivity analysis 100—101 161—162
Simple network management protocol (SNMP) 119
Store keeping unit (SKU) 50
Structured Query Language (SQL) 45 58
Symbolic data 55
Symmetrical multiprocessing (SMP) 46 58
System agents 119—120
System object model (SOM) 121
Taxonomies 50—51
Temporal difference learning network 185
Thermometer codes 54
Topologies, feedforward 65—66
Topologies, fully recurrent 67—68
Topologies, limited recurrent 66—67
Training 83—87 126 147—151 158—161 171—175 185
Training, avoiding overtraining 94
Training, classification process 83—84
Training, clustering process 84—85
Training, controlling process with learning parameters 87—91
Training, data representations and impact on time 55—56
Training, forecasting process 86—87
Training, managing data sets 56
Training, modeling process 85—86
Training, parameters 96
Training, supervised 88—89
Training, unsupervised 89—91
transaction processing 110—111
Translating, symbolic to numeric 51
User interface agents 118—119
Variables, discrete 53
Variables, fuzzy 195
Variables, linguistic 195
Visualization 102—106 187—188
Visualization, clustering 105—106
Visualization, Hinton diagram 105
Visualization, neural network graphics 103—104
Visualization, segmentation 105—106
Visualization, standard graphics 102—103
Work flow agents 119
World Wide Web (WWW) 118
Zadeh, Dr. Lotfi 192
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