The Resource Data mining methods for knowledge discovery, by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski

Data mining methods for knowledge discovery, by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski

Label
Data mining methods for knowledge discovery
Title
Data mining methods for knowledge discovery
Statement of responsibility
by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski
Creator
Contributor
Author
Subject
Genre
Language
  • eng
  • eng
Summary
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
Member of
Cataloging source
MiAaPQ
http://library.link/vocab/creatorName
Cios, Krzysztof J
Dewey number
005.74
Illustrations
illustrations
Image bit depth
0
Index
index present
Language note
English
LC call number
QA76.9.D3
LC item number
.C567 1998
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorDate
1953-
http://library.link/vocab/relatedWorkOrContributorName
  • Pedrycz, Witold
  • Świniarski, Roman
Series statement
Kluwer International Series in Engineering and Computer Science
http://library.link/vocab/subjectName
  • Database management
  • Data mining
Label
Data mining methods for knowledge discovery, by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski
Instantiates
Publication
Copyright
Note
Bibliographic Level Mode of Issuance: Monograph
Antecedent source
mixed
Bibliography note
Includes bibliographical references at the end of each chapters and index
Carrier category
online resource
Carrier category code
cr
Color
not applicable
Content category
text
Content type code
txt
Contents
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. Probabilities of 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 Objective Function—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
Dimensions
unknown
Extent
1 online resource (XXI, 495 p.)
File format
multiple file formats
Form of item
online
Isbn
9781461555896
Level of compression
uncompressed
Media category
computer
Media type code
c
Other control number
10.1007/978-1-4615-5589-6
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3400000000096269
  • (SSID)ssj0001007704
  • (PQKBManifestationID)11564940
  • (PQKBTitleCode)TC0001007704
  • (PQKBWorkID)10951962
  • (PQKB)11675787
  • (DE-He213)978-1-4615-5589-6
  • (MiAaPQ)EBC3081844
  • (EXLCZ)993400000000096269
Label
Data mining methods for knowledge discovery, by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski
Publication
Copyright
Note
Bibliographic Level Mode of Issuance: Monograph
Antecedent source
mixed
Bibliography note
Includes bibliographical references at the end of each chapters and index
Carrier category
online resource
Carrier category code
cr
Color
not applicable
Content category
text
Content type code
txt
Contents
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. Probabilities of 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 Objective Function—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
Dimensions
unknown
Extent
1 online resource (XXI, 495 p.)
File format
multiple file formats
Form of item
online
Isbn
9781461555896
Level of compression
uncompressed
Media category
computer
Media type code
c
Other control number
10.1007/978-1-4615-5589-6
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3400000000096269
  • (SSID)ssj0001007704
  • (PQKBManifestationID)11564940
  • (PQKBTitleCode)TC0001007704
  • (PQKBWorkID)10951962
  • (PQKB)11675787
  • (DE-He213)978-1-4615-5589-6
  • (MiAaPQ)EBC3081844
  • (EXLCZ)993400000000096269

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