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Tables of Contents for Computational Intelligence for Decision Support
Chapter/Section Title
Page #
Page Count
Preface
xi
Part I
Decision Support and computational Intelligence
1
10
Overview
1
1
The Need for decision Support Agents
1
1
Computerized Decision Support Mechanisms
2
1
Computational Intelligence for Decision Support
3
1
A Remark on Terminology
3
2
Data, Information and Knowledge
5
1
Issues to be Discussed in this Book
6
5
Summary
8
1
Self-examination Questions
8
1
References
9
2
Search and Representation
11
32
Overview
11
1
Sample Problems and Applications of computational Intelligence
11
5
Some Simple Examples
11
3
Applications
14
2
Definition of Computational Intelligence
16
2
Historical Development of Computational Intelligence
16
1
Computational Intelligence as Agent-Based Problem Solving
16
1
Measuring the Intelligence: Turing Test
17
1
Basic Assumptions of Computational Intelligence
18
3
Symbolism
18
1
Sequential or Parallel
19
1
Logic-based Approach
20
1
Human Intelligence as Metaphor
20
1
Summary
21
1
Basic Storage and Search Structures
21
3
Abstract Data Types and Data Structures
21
1
Linear Structures: Lists, Stacks, Queues and Priority Queues
22
1
Trees
22
1
Index Structures for Data Access
23
1
Discrimination Trees for Information Retrieval
23
1
Graphs
23
1
Remarks on Search Operation
24
1
Problem Solving Using Search
24
2
Meanings of Search
24
1
State space Search
25
1
Remarks on Scaling Up
26
1
Representing Knowledge for Search
26
1
Levels of Abstraction In Computational Intelligence Problem Solving
26
1
Using Abstract Levels
27
1
Programming Languages for Computational Intelligence
28
1
State Space Search
29
7
Uninformed Search (Blind Search)
29
3
Heuristic Search
32
4
Remark on Constraint-Based Search
36
1
Planning And Machine Learning as Search
37
6
Planning as Search
37
1
Symbol-Based Machine Learning as Search
38
1
Summary
39
1
Self-examination Questions
40
1
References
41
2
Predicate Logic
43
26
Overview
43
1
First Order Predicate Logic
43
10
Basics
43
1
Propositional calculus
44
1
Predicates
45
2
Quantifiers
47
1
Knowledge Base
47
1
Inference Rules
48
1
Substitution, Unification, Most General Unifier
49
1
Resolution -- The Basic Idea
49
4
Prolog for Computational Intelligence
53
10
Basics of Prolog
53
5
Sample Prolog Programs
58
4
Summary of Important Things About Prolog
62
1
Abduction and Induction
63
1
Other Forms of Reasoning
63
1
Induction
63
1
Abduction
64
1
Nonmonotonic Reasoning
64
5
Meaning of Nonmonotonic Reasoning
64
1
Commonsense Reasoning
65
1
Circumscription
66
1
Summary of Nonmonotonic Reasoning
67
1
Summary
67
1
Self-examination Questions
68
1
References
68
1
Relations as Predicates
69
34
Overview
69
1
The Concept of Relation
69
1
Overview of Relational Data Model
70
2
Schema and instance
70
1
Declarative and Procedural Languages
71
1
Relational Algebra
72
5
Preview of Relational Algebra
72
1
How to from a Relational Algebra Query from a Given English Query
73
1
Relational Algebra: fundamental Operators
74
1
Relational Algebra: Additional Operators
74
2
Combined Use of Operators
76
1
Extended RA Operations
77
1
Relational Views and Integrity Constraints
77
2
Virtual Views and Materialized Views
77
1
Integrity Constraints
78
1
Functional Dependencies
79
4
Definition of Functional Dependency
80
1
Keys and Functional Dependencies
80
1
Inference Rules: Armstrong Axioms
81
1
Closures and Canonical cover
81
1
Algorithms for Finding Keys from Functional Dependencies
82
1
Referential Integrity
83
1
Basics of Relational Database Design
83
7
What is the Meaning of a Good Design and Why Study It?
83
2
Boyce-Codd Normal Form (BCNF) and Third Normal Form (3NF)
85
1
Remarks on Normal Forms and Denormalization
86
1
Desirable Features for Decomposition-``Global'' Design Criteria
87
1
Decomposition Algorithms
88
2
Multivalued Dependencies
90
3
Various Forms of Dependencies
90
1
Multivalued Dependencies
91
1
Fourth Normal Form (4NF)
92
1
Remark on Object-Oriented Logical Data Modeling
93
1
Basics of Deductive Databases
94
5
Limitation of RA and SQL
94
1
Basics of Datalog
94
3
Deductive Query Evaluation
97
2
Knowledge Representation Meets Databases
99
4
Summary
100
1
Self-examination Questions
101
1
References
101
2
Retrieval Systems
103
38
Overview
103
1
Database management systems (DBMS)
104
2
Basics of Database Management Systems
104
1
Three Levels of Data Abstraction
104
1
Schema Versus Instances
105
1
Data Models
105
1
Database Languages
106
1
Components of Database Management Systems
106
1
Commercial Languages for Data Management Systems
106
5
Basic Remarks on Commercial Languages
106
1
Basic Structure of SQL Query
107
1
Examples of SQL Queries
107
1
Writing Sample SQL Queries
108
1
Working with SQL Programs:General Steps
109
1
Remarks on Integrity Constraints
109
1
Aggregate Functions
110
1
Remarks on Enhancement of SQL
110
1
Basics of Physical Database Design
111
2
Storage Media
111
1
File Structures and Indexing
112
1
Tuning Database Schema
113
1
An Overview of Query Processing and Transaction Processing
113
2
Query processing
113
1
Basics of Transaction Processing
114
1
How Transaction Processing is Related to Query Processing
114
1
Information Retrieval (IR)
115
3
Differences Between DBMS and IR Systems
115
1
Basics of Information Retrieval
115
2
Web Searching, Database Retrieval, and IR
117
1
Data Warehousing
118
4
Basics of Parallel and Distributed Databases
118
2
Data Warehousing and Decision Support
120
1
Middleware
121
1
Rule-Based Expert Systems
122
12
From Data and Information Retrieval to Knowledge Retrieval
122
1
Deductive Retrieval Systems
122
1
Deductive Retrieval Systems
123
1
Relationship with Key Interests in Computational Intelligence
124
1
Basics of Expert Systems
124
1
Production System Model
124
4
Knowledge Engineering
128
1
Building Rule-Based Expert systems
129
3
Some Other Aspects
132
1
CLIPS: A Brief Overview
133
1
Knowledge management and Ontologies
134
7
What is Knowledge Management?
134
1
Information Technology for Knowledge Management
135
1
Data and Knowledge Management Ontologies
136
1
Summary
137
1
Self-Examination Questions
137
1
References
138
3
Conceptual Data and Knowledge Modeling
141
22
Overview
141
1
Entity-Relationship Modeling
141
7
What is the Entity-Relationship (ER) Approach?
141
1
A Simple Example
142
1
Major constructs
143
1
Some Important concepts
143
1
Design Issues in ER Modeling
144
1
Mapping ER Diagrams into Relations
145
1
Keys in Converted Tables
145
1
An Example: A Banking Enterprise
146
1
Extended ER Features and Relationship with Object-Oriented Modeling
147
1
Remark on Legacy Data Models
148
1
Knowledge Modeling for Knowledge Representation
149
1
Structured Knowledge Representation
150
1
Some Important Issues Involved in Knowledge Representation and Reasoning
150
1
Basics of Structured Knowledge Representation Schemes
151
1
Frame Systems
151
2
Basics of Frames
151
1
Classes, Subclasses and Instances
152
1
Inheritance, Multi-Level and Multiple Inheritance
152
1
Conceptual Graphs
153
6
What is a Conceptual Graph?
153
2
Using Linear Form to Represent conceptual Graphs
155
1
Operations
155
1
Logic-Related Aspects
156
3
Remarks on Synergy of Frame Systems, Conceptual Graphs and Object Orientation
159
1
User Modeling and Flexible Inference control
159
4
Summary
160
1
Self-examination Questions
161
1
References
161
2
Part II
Reasoning As Extended Retrieval
163
24
Overview
163
1
Beyond Exact Retrieval
163
3
Some Forms of Non-Exact Retrieval
163
2
Basics of Analogical Reasoning
165
1
Reasoning as Query-Invoked Memory Re-Organization
166
18
Reasoning as Extended Retrieval
166
1
Structure Mapping for Suggestion-Generation
166
1
Document Storage and Retrieval Through Relational Database Operations
167
10
Generating Suggestions
177
7
Summary
184
3
Self-examination Questions
184
1
References
185
2
Computational Creativity and Computer Assisted Human Intelligence
187
24
Overview
187
1
Computational Aspects of Creativity
187
3
Remarks on Creativity
187
1
Theoretical Foundation for Stimulating Human Thinking
188
1
Creativity in Decision Support Systems
189
1
Idea Processors
190
5
Basics of Idea Processors
190
2
Common components in Idea Processors
192
1
How Idea Processors Work
192
1
The Nature of Idea Processors
193
2
Retrospective analysis for Scientific Discovery and Technical Invention
195
6
Retrospective Analysis of Technical Invention
195
2
Retrospective Analysis for Knowledge-Based Idea Generation of New Artifacts
197
1
A Prolog Program to Explore Idea Generation
198
3
Combining Creativity with Expertise
201
10
The Need for combining Creativity with Expertise
201
1
Strategic Knowledge as Knowledge Related to Creativity
201
2
Studying Strategic Heuristics of Creative Knowledge
203
1
Difficulties and Problems in Acquiring Strategic Heuristics
204
1
The Nature of Strategic Heuristics
205
1
Toward Knowledge-Based Architecture Combining Creativity and Expertise
206
1
Summary
207
1
Self-examination Questions
208
1
References
208
3
Conceptual Queries and Intensional Answering
211
14
Overview
211
1
A Review of Question Answering Systems
211
1
What is a Question Answering Systems?
211
1
Some Features of Question Answering
212
1
Intensional Answering and conceptual Query
212
6
Meaning of Intensional Answers
213
1
Intensional Answering Using Knowledge Discovery
213
2
Conceptual Query Answering
215
1
Duality Between Conceptual Queries and Intensional Answers
216
2
An Approach for Intensional Conceptual Query Answering
218
7
Introduction
218
1
Constructing an Abstract Database for Intensional Answers
219
2
Generating Intensional Answers for conceptual Queries
221
1
Method for Intensional Conceptual Query Answering
222
1
Summary
223
1
Self-examination Questions
223
1
References
223
2
Part III
From Machine Learning to Data Mining
225
36
Overview
225
1
Basics of machine Learning
226
1
Machine Learning: Definition and Approaches
226
1
Inductive Learning
227
5
Generalization for Induction
227
1
Candidate Elimination Algorithm
227
1
ID3 Algortithm and C4.5
228
4
Efficiency and Effectiveness of Inductive Learning
232
1
Inductive Bias
232
1
Theory of Learnability
232
1
Other Machine Learning Approaches
233
6
Machine learning in Neural Networks
233
2
Evolutionary Algorithms for Machine Learning
235
4
Summary of machine Learning Methods
239
1
Features of Data Mining
239
7
The Popularity of Data Mining
239
1
KDD Versus Data Mining
240
2
Data Mining versus Machine Learning
242
1
Data Mining versus Extended Retrieval
243
1
Data Mining versus Statistic Analysis and Intelligent Data Analysis
244
1
Data Mining Mechanism: Data Mining from a Database Perspective
245
1
Summary of Features
245
1
Categorizing Data Mining Techniques
246
2
What is to be Discovered
246
1
Discovery or Prediction
246
1
Symbolic, Connectionism and Evolutionary algorithms
247
1
Classifying Data Mining Techniques
247
1
Association Rules
248
13
Terminology
248
3
Finding Association Rules Using Apriori Algorithm
251
2
More Advanced Studies of Association Rules
253
2
Summary
255
1
Self-examination Questions
255
1
References
256
5
Data Warehousing, OLAP and Data Mining
261
36
Overview
261
1
Data Mining in Data Warehouses
262
1
Decision Support Queries, Data Warehouse and OLAP
263
7
Decision Support Queries
263
1
Architecture of Data Warehouses
264
2
Basics of OLAP
266
4
Data Warehouse as Materialized Views and Indexing
270
7
Review of a Popular Definition
270
1
Materialized Views
271
2
Maintenance of Materialized Views
273
1
Normalization and Denormalization of Materialized Views
274
1
Indexing Techniques for Implementation
275
2
Remarks on Physical Design of Data Warehouses
277
1
Semantic Differences between Data Mining and OLAP
278
4
Different Types of Queries can be Answered at Different Levels
278
1
Aggregation Semantics
279
3
Nonmonotonic Reasoning in Data Warehousing Environment
282
1
Combining Data Mining and OLAP
283
5
An Architecture Combining OLA and Data Mining
283
1
Some Specific Issues
284
4
Conceptual Query Answering in Data Warehouses
288
2
Materialized Views and Inensional Answering
288
1
Rewriting conceptual Query using Materialized Views
289
1
Web Mining
290
7
Basic Approaches for Web Mining
290
1
Discovery Techniques on Web Transactions
291
2
Summary
293
1
Self-examination Questions
293
1
References
293
4
Reasoning Under Uncertainty
297
44
Overview
297
1
General Remarks on Uncertain Reasoning
298
3
Logic and Uncertainty
298
1
Different Types of Uncertainty and Ontologies of Uncertainty
299
1
Uncertainty and Search
300
1
Uncertainty Based on Probability Theory
301
13
Basics of Probability Theory
301
1
Bayesian Approach
302
1
Bayesian Networks
303
4
Bayesian Network Approach for Data Mining
307
3
A Brief Remark on Influence Diagram and Decision Theory
310
1
Probability Theory with Measured Belief and Disbelief
311
3
Fuzzy Set Theory
314
6
Fuzzy Sets
314
3
Fuzzy Set Operations
317
2
Resolution in Possibilistic Logic
319
1
Fuzzy Rules and fuzzy Expert Systems
320
6
Fuzzy Relations
320
1
Syntax and Semantics of Fuzzy Rules
321
3
Fuzzy Inference Methods
324
2
Using Fuzzy CLIPS
326
2
Fuzzy Controllers
328
7
Basics of Fuzzy Controller
327
2
Building Fuzzy Controller Using Fuzzy CLIPS
329
3
Fuzzy Controller Design Process
332
3
The nature of Fuzzy Logic
335
6
The Inconsistency of Fuzzy Logic
336
1
Why Fuzzy Logic has been Successful in Expert Systems
336
1
Implication to Mainstream Computational Intelligence
337
1
Summary
337
1
Self-examination Questions
338
1
References
339
2
Reduction and Reconstruction Approaches for Uncertain Reasoning and Data Mining
341
16
Overview
341
1
The Reduction-Reconstruction Duality
341
2
Reduction and Reconstruction Aspects in Fuzzy Set Theory
341
1
Reconstruction and Data Mining
342
1
Some Key Ideas of K-systems Theory and Rough Set Theory
343
2
Reconstructability Analysis using K-systems Theory
343
1
Reduction-Driven Approach in Rough Set Theory
344
1
K-Systems theory versus Rough Set Theory
345
1
Rough Sets Approach
345
6
Basic Idea of Rough Sets
345
1
Terminology
346
1
An Example
347
2
Rule Induction Using Rough Set Approach
349
1
Applications of Rough Sets
350
1
K-systems Theory
351
6
Summary
353
1
Self-examination Questions
354
1
References
354
3
Part IV
Toward Integrated Heuristic Decision Making
357
20
Overview
357
1
Integrated Problem Solving
357
2
High Level Heuristics for Problem Solving and Decision Support
359
5
A Return to General Problem Solver
359
1
Some High Level Heuristics
359
5
Summary of Heuristics
364
1
Meta-Issues for Decision Making
364
13
Meta Issues in Databases and Data Warehouses
364
2
Meta-Knowledge and Meta-Reasoning
366
5
Meta-Knowledge and Meta-Patterns in Data Mining
371
2
Meta-Learning
373
1
Summary and Remark on Meta-Issues
373
1
Summary
374
1
Self-examination Questions
374
1
References
375
2
Index
377