Linked e-resources
Details
Table of Contents
Foreword; Preface; Acknowledgements; Contents; Acronyms; Notations; 1 Fuzzy Set and Fuzzy Logic Theory in Brief; 1.1 From Crispness to Fuzziness; 1.2 Fuzzy Sets; 1.2.1 Properties of Fuzzy Sets; 1.2.2 Types of Fuzzy Sets (Membership Functions); 1.2.3 Operations on Fuzzy Sets; 1.2.4 Fuzzy Numbers and Fuzzy Arithmetic; 1.2.5 Measures of Fuzziness; 1.2.6 Fuzzy Relations; 1.3 Fuzzy Logic; 1.3.1 Fuzzy Conjunction ; 1.3.2 Fuzzy Negation ; 1.3.3 Fuzzy Disjunction ; 1.3.4 Fuzzy Implication ; 1.4 Linguistic Variables; 1.5 Fuzzy Quantifiers; 1.6 Some Remarks; References; 2 Fuzzy Queries.
2.1 From Crisp to Fuzzy Queries2.2 Construction of Fuzzy Sets for Flexible Conditions; 2.3 Converting Fuzzy Conditions to SQL Ones; 2.4 Calculation of Matching Degrees; 2.4.1 Independent Conditions Aggregated by the #x8D;And#x8E; Operator; 2.4.2 Fuzzy Preferences Among Atomic Query Conditions; 2.4.3 Answer to the Second Atomic Condition Depends on the Answer to the First One; 2.4.4 Constraints and Wishes; 2.4.5 Quantified Queries; 2.4.6 Querying Changes of Attributes over Time; 2.5 Empty and Overabundant Answers; 2.5.1 Empty Answer Problem; 2.5.2 Overabundant Answer Problem.
2.6 Some Issues Related to Practical RealizationReferences; 3 Linguistic Summaries; 3.1 Benefits and Protoforms of Linguistic Summarization; 3.2 The Basic Structure of LS; 3.3 Relative Quantifiers in Summaries; 3.4 LS with Restriction; 3.5 Mining Linguistic Summaries of Interest; 3.6 Quality Measures of LSs; 3.6.1 Quality Measures; 3.6.2 Aggregation of Quality Measures; 3.6.3 Influence of Constructed Fuzzy Sets and T-Norms on Quality; 3.7 Some Applicability of LS; 3.7.1 Quantified Queries (LS as a Nested Condition); 3.7.2 Generating IF-THEN Rules; 3.7.3 Estimation of Missing Values.
3.8 Building SummariesReferences; 4 Fuzzy Inference; 4.1 From Classical to Fuzzy Inference; 4.2 Fuzzy Inference; 4.2.1 Inference Process; 4.2.2 Defuzzification; 4.2.3 Illustrative Examples and Issues; 4.3 Fuzzy Inference Systems; 4.3.1 Mamdani Model (Logical Model); 4.3.2 Sugeno Model (Functional Model); 4.4 Fuzzy Rule-Based System Design; 4.5 Fuzzy Classification; 4.5.1 A View on Crisp Classification; 4.5.2 Managing Fuzzy Classification; 4.5.3 Fuzzy Classification by Fuzzy Queries; 4.6 Remarks to Applications; References; 5 Fuzzy Data in Relational Databases.
5.1 Classical Relational Databases5.2 Fuzziness in the Data; 5.3 Fuzzy Databases: An Overview; 5.4 Basic Model of Fuzzy Database; 5.4.1 Structure of Basic Model; 5.4.2 Querying Basic Model; 5.5 Fuzzy Data in Traditional Relational Databases Managed by Fuzzy Meta Model; 5.5.1 Creating Fuzzy Meta Model; 5.5.2 Storing and Representing Tuples; 5.5.3 Inserting Fuzziness into Existing Databases; 5.5.4 Managing Fuzziness in Data and in Inference Rules by the Same Database; 5.6 Querying Fuzzy Relational Databases; 5.6.1 Aggregation Functions in Queries; 5.6.2 Query Conditions.
2.1 From Crisp to Fuzzy Queries2.2 Construction of Fuzzy Sets for Flexible Conditions; 2.3 Converting Fuzzy Conditions to SQL Ones; 2.4 Calculation of Matching Degrees; 2.4.1 Independent Conditions Aggregated by the #x8D;And#x8E; Operator; 2.4.2 Fuzzy Preferences Among Atomic Query Conditions; 2.4.3 Answer to the Second Atomic Condition Depends on the Answer to the First One; 2.4.4 Constraints and Wishes; 2.4.5 Quantified Queries; 2.4.6 Querying Changes of Attributes over Time; 2.5 Empty and Overabundant Answers; 2.5.1 Empty Answer Problem; 2.5.2 Overabundant Answer Problem.
2.6 Some Issues Related to Practical RealizationReferences; 3 Linguistic Summaries; 3.1 Benefits and Protoforms of Linguistic Summarization; 3.2 The Basic Structure of LS; 3.3 Relative Quantifiers in Summaries; 3.4 LS with Restriction; 3.5 Mining Linguistic Summaries of Interest; 3.6 Quality Measures of LSs; 3.6.1 Quality Measures; 3.6.2 Aggregation of Quality Measures; 3.6.3 Influence of Constructed Fuzzy Sets and T-Norms on Quality; 3.7 Some Applicability of LS; 3.7.1 Quantified Queries (LS as a Nested Condition); 3.7.2 Generating IF-THEN Rules; 3.7.3 Estimation of Missing Values.
3.8 Building SummariesReferences; 4 Fuzzy Inference; 4.1 From Classical to Fuzzy Inference; 4.2 Fuzzy Inference; 4.2.1 Inference Process; 4.2.2 Defuzzification; 4.2.3 Illustrative Examples and Issues; 4.3 Fuzzy Inference Systems; 4.3.1 Mamdani Model (Logical Model); 4.3.2 Sugeno Model (Functional Model); 4.4 Fuzzy Rule-Based System Design; 4.5 Fuzzy Classification; 4.5.1 A View on Crisp Classification; 4.5.2 Managing Fuzzy Classification; 4.5.3 Fuzzy Classification by Fuzzy Queries; 4.6 Remarks to Applications; References; 5 Fuzzy Data in Relational Databases.
5.1 Classical Relational Databases5.2 Fuzziness in the Data; 5.3 Fuzzy Databases: An Overview; 5.4 Basic Model of Fuzzy Database; 5.4.1 Structure of Basic Model; 5.4.2 Querying Basic Model; 5.5 Fuzzy Data in Traditional Relational Databases Managed by Fuzzy Meta Model; 5.5.1 Creating Fuzzy Meta Model; 5.5.2 Storing and Representing Tuples; 5.5.3 Inserting Fuzziness into Existing Databases; 5.5.4 Managing Fuzziness in Data and in Inference Rules by the Same Database; 5.6 Querying Fuzzy Relational Databases; 5.6.1 Aggregation Functions in Queries; 5.6.2 Query Conditions.