1 Data Mining and Knowledge Discovery. - 1. 1 Data Mining and Information Age: Emerging Quests. - 1. 2 Defining Knowledge Discovery. - 1. 3 Architectures of Knowledge Discovery. - 1. 4 Knowledge Representation. - 1. 5 Main Types of Revealed Patterns. - 1. 6 Basic Models of Data Mining. - 1. 7 Knowledge Discovery and Related Research Areas. - 1. 8 Main Features of a Knowledge Discovery Process. - 1. 9 Coping with Reality. Sampling in Databases. - 1. 10 Selected Examples of Knowledge Discovery Systems. - 1. 11 Summary. - References. - Additional Readings. - 2 Rough Sets. - 2. 1 Introduction. - 2. 2 Information System. - 2. 3 Indiscernibility Relation. - 2. 4 Discernibility Matrix. - 2. 5 Decision Tables. - 2. 6 Approximation of Sets. Approximation Space. - 2. 7 Accuracy of Approximation. - 2. 8 Approximation and Accuracy of Classification. - 2. 9 Classification and Reduction. - Reduct and Core. - 2. 10 Decision Rules. - 2. 11 Dynamic Reducts. - 2. 12 Summary. - 2. 13 Exercises. - References. - Appendix A2: Algorithms for Finding Minimal Subsets. - 3 Fuzzy Sets. - 3. 1 Introduction. - 3. 2 Basic Definition. - 3. 3 Types of Membership Functions. - 3. 4 Characteristics of a Fuzzy Set. - 3. 5 Membership Function Determination. - 3. 6 Fuzzy Relations. - 3. 7 Set Theory Operations and Their Properties. - 3. 8 The Extension Principle and Fuzzy Arithmetic. - 3. 9 InformationBased Characteristics of Fuzzy Sets. - 3. 10 Numerical Representation of Fuzzy Sets. - 3. 11 Rough Sets and Fuzzy Sets. - 3. 12 The Frame of Cognition. - 3. 13 Probability and Fuzzy Sets. - 3. 14 Summary. - 3. 15 Exercises. - References. - 4 Bayesian Methods. - 4. 1 Introduction. - 4. 2 Basics of Bayesian Methods. - 4. 3 Involving Object Features in Classification. - 4. 4 Bayesian Classification a General Case. - 4. 5 Statistical Classification Minimizing Risk. - 4. 6 Decision Regions. Probabilitiesof Errors. - 4. 7 Discriminant Functions. - 4. 8 Estimation of Probability Densities. - 4. 9 Probabilistic Neural Network (PNN). - 4. 10 Constraints in Design. - 4. 11 Summary. - 4. 12 Exercises. - References. - 5 Evolutionary Computing. - 5. 1 Genetic Algorithms. Concept and Algorithmic Aspects. - 5. 2 Fundamental Components of GAs 196 Encoding and Decoding. - 5. 3 GA. Formal Definition of Genetic Algorithms. - 5. 4 Schemata Theorem: a Cnceptual Backbone of Gas. - 5. 5 Genetic Computing. Further Enhancement. - 5. 6 Exploration and Exploitation of the Search Space. - 5. 7 Experimental Studies. - 5. 8 Classes of Evolutionary Computation. - 5. 9 Genetic Optimization of Rule-Based Description of Data: Pittsburgh and Michigan Approaches. - 5. 10 Summary. - 5. 11 Exercises. - References. - 6 Machine Learning. - 6. 1 Introduction. - 6. 2 Introduction to Generation of Hypotheses. - 6. 3 Overfitting. - 6. 4 Rule Algorithms. - 6. 5 Decison Tree Algorithms. - 6. 6 Hybrid Algorithms. - 6. 7 Discretization of Continuous-Valued Attributes. - 6. 7. 1 Information-Theoretic Discretization Methods. - 6. 8 Hypothesis Evaluation. - 6. 9 Comparison of the Three Families of Algorithms. - 6. 10 Machine Learning in Knowledge Discovery. - 6. 11 Machine Learning and Rough Sets. - 6. 12 Summary. - 6. 13 Exercises. - References. - Appendix A6: Diagnosing Coronary Artery Disease (CAD). - References. - 7 Neural Networks. - 7. 1 Introduction. - 7. 2 Radial Basis Function (RBF) Network. - 7. 3 RBF Networks in Knowledge Discovery. - 7. 4 Kohonen's Self Organizing Map(SOM)Network. - 7. 5 Image Recognition Neural Network (IRNN) 357 Sensory Layer. - 7. 6 Summary. - 7. 7 Exercises. - References. - Appendix A7: Image Similarity(IS) Measure. - 8 Clustering. - 8. 1 Unsupervised Learning: a General Taxonomy and Related Algorithmic Aspects. - 8. 2 Hierarchical Clustering. - 8. 3 ObjectiveFunctionBased Clustering. - 8. 4 Clustering Methods and Data Mining. - 8. 5 Hierarchical Clustering in Building Associations in the Data. - 8. 6 Clustering under Partial Supervision in Data Mining. - 8. 7 A Neural Realization of Similarity Between Patterns. - 8. 8 Numerical Experiments. - 8. 9 Summary. - 8. 10 Exercises. - References. - 9 Preprocessing. - 9. 1 Patterns and Features. - 9. 2 Preprocessing Operations. - 9. 3 Principal Component Analysis Feature Extraction and Reduction. - 9. 4 Supervised Feature Reduction Based on Fisher's Linear Discriminant Analysis. - 9. 5 Sequence of Karhunen-Loeve and Fisher's Linear Discriminant Projections. - 9. 6 Feature Selection. - 9. 7 Numerical Experiments Texture Image Classification. - 9. 8 Summary. - 9. 9 Exercises. - References. Language: English