## Intelligent Data Analysis: An Introduction
This monograph is a detailed introductory presentation of the key classes of intelligent data analysis (IDA) methods. The 12 coherently written chapters by leading experts provide complete coverage of the core issues. The previous edition was completely revised and a new chapter on kernel methods and support vector machines and a chapter on visualization techniques were added. The revised chapters from the original edition cover classical statistics issues, ranging from the basic concepts of probability through general notions of inference to advanced multivariate and time-series methods, and provide a detailed discussion of the increasingly important Bayesian approaches. The remaining chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions to the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a higher-level overview of the IDA processes, illustrating the breadth of application of the presented ideas. The second edition features an extensive index, which makes this volume also useful as a quick reference on the key techniques in intelligent data analysis. |

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### Contents

Introduction | 1 |

12 How the Computer Is Changing Thingsthe Merger of Disciplines | 4 |

13 The Nature of Data | 8 |

14 Modern Data Analytic Tools | 12 |

15 Conclusion | 14 |

Statistical Concepts | 16 |

22 Probability | 18 |

23 Sampling and Sampling Distributions | 29 |

82 Fundamentals | 270 |

83 Multilayer Feedforward Neural Networks | 278 |

84 Learning and Generalization | 283 |

85 Radial Basis Function Networks | 292 |

86 Competitive Learning | 300 |

87 Principal Components Analysis and Neural Networks | 307 |

88 Time Series Analysis | 312 |

89 Conclusion | 319 |

24 Statistical Inference | 33 |

25 Prediction and Prediction Error | 46 |

26 Resampling | 57 |

27 Conclusion | 68 |

Statistical Methods | 69 |

32 Generalized Linear Models | 70 |

33 Special Topics in Regression Modelling | 93 |

34 Classical Multivariate Analysis | 100 |

35 Conclusion | 129 |

Bayesian Methods | 131 |

42 The Bayesian Paradigm | 132 |

43 Bayesian Inference | 135 |

44 Bayesian Modeling | 143 |

45 Bayesian Networks | 153 |

46 Conclusion | 167 |

Support Vector and Kernel Methods | 169 |

Kernel Perceptron | 170 |

52 Overfitting and Generalization Bounds | 176 |

53 Support Vector Machines | 181 |

54 Kernel PCA and CCA | 194 |

55 Conclusion | 196 |

Analysis of Time Series | 198 |

62 Linear Systems Analysis | 202 |

63 Nonlinear Dynamics Basics | 207 |

64 DelayCoordinate Embedding | 213 |

65 Examples | 218 |

66 Conclusion | 226 |

Rule Induction | 229 |

72 Propositional rule learning | 232 |

73 Rule learning as search | 236 |

74 Evaluating the quality of rules | 242 |

75 Propositional rule induction at work | 246 |

76 Learning firstorder rules | 250 |

77 Some ILP systems at work | 262 |

78 Conclusion | 267 |

Neural Networks | 268 |

Fuzzy Logic | 321 |

92 Basics of Fuzzy Sets and Fuzzy Logic | 322 |

93 Extracting Fuzzy Models from Data | 336 |

94 Fuzzy Decision Trees | 346 |

95 Conclusion | 350 |

Stochastic Search Methods | 351 |

102 Stochastic Search by Simulated Annealing | 354 |

103 Stochastic Adaptive Search by Evolution | 360 |

104 Evolution Strategies | 362 |

105 Genetic Algorithms | 374 |

106 Genetic Programming | 390 |

107 Conclusion and Summary | 400 |

Visualization | 403 |

112 Classification of Visual Data Analysis Techniques | 405 |

113 Data Type to be Visualized | 406 |

114 Visualization Techniques | 411 |

115 Interaction Techniques | 414 |

116 Specific Visual Data Analysis Techniques | 418 |

117 Conclusion | 426 |

Systems and Applications | 428 |

122 Diversity of IDA Applications | 430 |

123 Several Development Issues | 436 |

124 Conclusion | 442 |

Tools | 445 |

A2 Tools for explorationmodeling | 447 |

A3 Tools for Text and Web Mining | 454 |

A4 Data Analysis Suites | 456 |

A5 Conclusion | 464 |

InformationTheoretic Tree and Rule Induction | 465 |

B2 Decision Tree Induction | 468 |

B3 Rule Induction | 470 |

474 | |

501 | |

Author Addresses | 513 |