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Data Mining Methods for Knowledge Discovery de Krzysztof J. Cios

Data Mining Methods for Knowledge Discovery: The Springer International Series in Engineering and Computer Science, cartea 458

Autor Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski
en Limba Engleză Hardback – 31 aug 1998
Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography.
Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.
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Specificații

ISBN-13: 9780792382522
ISBN-10: 0792382528
Pagini: 524
Ilustrații: XXI, 495 p.
Dimensiuni: 155 x 235 x 34 mm
Greutate: 0.84 kg
Ediția:1998
Editura: Springer Us
Colecția Springer
Seria The Springer International Series in Engineering and Computer Science

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

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 Information—Based 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 ObjectiveFunction—Based 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.