Computational Intelligence for Decision Support

Front Cover
CRC Press, Nov 24, 1999 - Computers - 400 pages
Intelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making.

Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest.

The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

DECISION SUPPORT AND COMPUTATIONAL INTELLIGENCE
1
13 COMPUTERIZED DECISION SUPPORT MECHANISMS
2
14 COMPUTATIONAL INTELLIGENCE FOR DECISION SUPPORT
3
16 DATA INFORMATION AND KNOWLEDGE
5
17 ISSUES TO BE DISCUSSED IN THIS BOOK
6
SUMMARY
8
REFERENCES
9
SEARCH AND REPRESENTATION
11
722 BASICS OF ANALOGICAL REASONING
165
73 REASONING AS QUERYINVOKED MEMORY REORGANIZATION
166
733 DOCUMENT STORAGE AND RETRIEVAL THROUGH RELATIONAL DATABASE OPERATIONS
167
734 GENERATING SUGGESTIONS
177
74 SUMMARY
184
REFERENCES
185
COMPUTATIONAL CREATIVITY AND COMPUTER ASSISTED HUMAN INTELLIGENCE
187
822 THEORETICAL FOUNDATION FOR STIMULATING HUMAN THINKING
188

222 APPLICATIONS
14
23 DEFINITION OF COMPUTATIONAL INTELLIGENCE
16
TURING TEST
17
24 BASIC ASSUMPTIONS OF COMPUTATIONAL INTELLIGENCE
18
242 SEQUENTIAL OR PARALLEL
19
243 LOGICBASED APPROACH
20
245 SUMMARY
21
LISTS STACKS QUEUES AND PRIORITY QUEUES
22
254 INDEX STRUCTURES FOR DATA ACCESS
23
257 REMARKS ON SEARCH OPERATION
24
262 STATE SPACE SEARCH
25
263 REMARKS ON SCALING UP
26
272 USING ABSTRACT LEVELS
27
273 PROGRAMMING LANGUAGES FOR COMPUTATIONAL INTELLIGENCE
28
28 STATE SPACE SEARCH
29
282 HEURISTIC SEARCH
32
29 REMARK ON CONSTRAINTBASED SEARCH
36
210 PLANNING AND MACHINE LEARNING AS SEARCH
37
2102 SYMBOLBASED MACHINE LEARNING AS SEARCH
38
SUMMARY
39
SELFEXAMINATION QUESTIONS
40
REFERENCES
41
PREDICATE LOGIC
43
322 PROPOSITIONAL CALCULUS
44
323 PREDICATES
45
324 QUANTIFIERS
47
326 INFERENCE RULES
48
327 SUBSTITUTION UNIFICATION MOST GENERAL UNIFIER
49
33 PROLOG FOR COMPUTATIONAL INTELLIGENCE
53
332 SAMPLE PROLOG PROGRAMS
58
333 SUMMARY OF IMPORTANT THINGS ABOUT PROLOG
62
34 ABDUCTION AND INDUCTION
63
343 ABDUCTION
64
352 COMMONSENSE REASONING
65
353 CIRCUMSCRIPTION
66
354 SUMMARY OF NONMONOTONIC REASONING
67
SELFEXAMINATION QUESTIONS
68
RELATIONS AS PREDICATES
69
43 OVERVIEW OF RELATIONAL DATA MODEL
70
432 DECLARATIVE AND PROCEDURAL LANGUAGES
71
44 RELATIONAL ALGEBRA
72
442 HOW TO FORM A RELATIONAL ALGEBRA QUERY FROM A GIVEN ENGLISH QUERY
73
FUNDAMENTAL OPERATORS
74
445 COMBINED USE OF OPERATORS
76
446 EXTENDED RA OPERATIONS
77
452 INTEGRITY CONSTRAINTS
78
46 FUNCTIONAL DEPENDENCIES
79
461 DEFINITION OF FUNCTIONAL DEPENDENCY
80
ARMSTRONG AXIOMS
81
465 ALGORITHMS FOR FINDING KEYS FROM FUNCTIONAL DEPENDENCIES
82
466 REFERENTIAL INTEGRITY
83
472 BOYCECODD NORMAL FORM BCNF AND THIRD NORMAL FORM 3NF
85
473 REMARKS ON NORMAL FORMS AND DENORMALIZATION
86
474 DESIRABLE FEATURES FOR DECOMPOSITION GLOBAL DESIGN CRITERIA
87
475 DECOMPOSITION ALGORITHMS
88
48 MULTIVALUED DEPENDENCIES
90
482 MULTIVALUED DEPENDENCIES
91
483 FOURTH NORMAL FORM 4NF
92
49 REMARK ON OBJECTORIENTED LOGICAL DATA MODELING
93
410 BASICS OF DEDUCTIVE DATABASES
94
4103 DEDUCTIVE QUERY EVALUATION
97
411 KNOWLEDGE REPRESENTATION MEETS DATABASES
99
SUMMARY
100
SELFEXAMINATION QUESTIONS
101
RETRIEVAL SYSTEMS
103
52 DATABASE MANAGEMENT SYSTEMS DBMS
104
523 SCHEMA VERSUS INSTANCES
105
525 DATABASE LANGUAGES
106
532 BASIC STRUCTURE OF SQL QUERY
107
534 WRITING SIMPLE SQL QUERIES
108
GENERAL STEPS
109
537 AGGREGATE FUNCTIONS
110
54 BASICS OF PHYSICAL DATABASE DESIGN
111
542 FILE STRUCTURES AND INDEXING
112
543 TUNING DATABASE SCHEMA
113
552 BASICS OF TRANSACTION PROCESSING
114
56 INFORMATION RETRIEVAL IR
115
563 WEB SEARCHING DATABASE RETRIEVAL AND IR
117
57 DATA WAREHOUSING
118
572 DATA WAREHOUSING AND DECISION SUPPORT
120
573 MIDDLEWARE
121
58 RULEBASED EXPERT SYSTEMS
122
582 DEDUCTIVE RETRIEVAL SYSTEMS
123
583 RELATIONSHIP WITH KEY INTERESTS IN COMPUTATIONAL INTELLIGENCE
124
586 KNOWLEDGE ENGINEERING
128
587 BUILDING RULEBASED EXPERT SYSTEMS
129
588 SOME OTHER ASPECTS
132
A BRIEF OVERVIEW
133
59 KNOWLEDGE MANAGEMENT AND ONTOLOGIES
134
592 INFORMATION TECHNOLOGY FOR KNOWLEDGE MANAGEMENT
135
593 DATA AND KNOWLEDGE MANAGEMENT ONTOLOGIES
136
SUMMARY
137
REFERENCES
138
CONCEPTUAL DATA AND KNOWLEDGE MODELING
141
622 A SIMPLE EXAMPLE
142
623 MAJOR CONSTRUCTS
143
625 DESIGN ISSUES IN ER MODELING
144
626 MAPPING ER DIAGRAMS INTO RELATIONS
145
A BANKING ENTERPRISE
146
629 EXTENDED ER FEATURES AND RELATIONSHIP WITH OBJECTORIENTED MODELING
147
63 REMARK ON LEGACY DATA MODELS
148
64 KNOWLEDGE MODELING FOR KNOWLEDGE REPRESENTATION
149
65 STRUCTURED KNOWLEDGE REPRESENTATION
150
652 BASICS OF STRUCTURED KNOWLEDGE REPRESENTATION SCHEMES
151
662 CLASSES SUBCLASSES AND INSTANCES
152
67 CONCEPTUAL GRAPHS
153
672 USING LINEAR FORM TO REPRESENT CONCEPTUAL GRAPHS
155
674 LOGICRELATED ASPECTS
156
675 REMARKS ON SYNERGY OF FRAME SYSTEMS CONCEPTUAL GRAPHS AND OBJECT ORIENTATION
159
SUMMARY
160
SELFEXAMINATION QUESTIONS
161
REASONING AS EXTENDED RETRIEVAL
163
823 CREATIVITY IN DECISION SUPPORT SYSTEMS
189
83 IDEA PROCESSORS
190
832 COMMON COMPONENTS IN IDEA PROCESSORS
192
834 THE NATURE OF IDEA PROCESSORS
193
84 RETROSPECTIVE ANALYSIS FOR SCIENTIFIC DISCOVERY AND TECHNICAL INVENTION
195
842 RETROSPECTIVE ANALYSIS FOR KNOWLEDGEBASED IDEA GENERATION OF NEW ARTIFACTS
197
843 A PROLOG PROGRAM TO EXPLORE IDEA GENERATION
198
85 COMBINING CREATIVITY WITH EXPERTISE
201
853 STUDYING STRATEGIC HEURISTICS OF CREATIVE KNOWLEDGE
203
854 DIFFICULTIES AND PROBLEMS IN ACQUIRING STRATEGIC HEURISTICS
204
855 THE NATURE OF STRATEGIC HEURISTICS
205
856 TOWARD KNOWLEDGEBASED ARCHITECTURE COMBINING CREATIVITY AND EXPERTISE
206
SUMMARY
207
SELFEXAMINATION QUESTIONS
208
CONCEPTUAL QUERIES AND INTENSIONAL ANSWERING
211
922 SOME FEATURES OF QUESTION ANSWERING
212
931 MEANING OF INTENSIONAL ANSWERS
213
933 CONCEPTUAL QUERY ANSWERING
215
934 DUALITY BETWEEN CONCEPTUAL QUERIES AND INTENSIONAL ANSWERS
216
94 AN APPROACH FOR INTENSIONAL CONCEPTUAL QUERY ANSWERING
218
942 CONSTRUCTING AN ABSTRACT DATABASE FOR INTENSIONAL ANSWERS
219
943 GENERATING INTENSIONAL ANSWERS FOR CONCEPTUAL QUERIES
221
944 METHOD FOR INTENSIONAL CONCEPTUAL QUERY ANSWERING
222
SUMMARY
223
FROM MACHINE LEARNING TO DATA MINING
225
102 BASICS OF MACHINE LEARNING
226
103 INDUCTIVE LEARNING
227
1033 IDS ALGORITHM AND C45
228
104 EFFICIENCY AND EFFECTIVENESS OF INDUCTIVE LEARNING
232
105 OTHER MACHINE LEARNING APPROACHES
233
1052 EVOLUTIONARY ALGORITHMS FOR MACHINE LEARNING
235
1053 SUMMARY OF MACHINE LEARNING METHODS
239
1062 KDD VERSUS DATA MINING
240
1063 DATA MINING VERSUS MACHINE LEARNING
242
1064 DATA MINING VERSUS EXTENDED RETRIEVAL
243
1065 DATA MINING VERSUS STATISTIC ANALYSIS AND INTELLIGENT DATA ANALYSIS
244
DATA MINING FROM A DATABASE PERSPECTIVE
245
107 CATEGORIZING DATA MINING TECHNIQUES
246
1073 SYMBOLIC CONNECTIONISM AND EVOLUTIONARY ALGORITHMS
247
108 ASSOCIATION RULES
248
1082 FINDING ASSOCIATION RULES USING APRIORI ALGORITHM
251
1083 MORE ADVANCED STUDIES OF ASSOCIATION RULES
253
SUMMARY
255
REFERENCES
256
DATA WAREHOUSING OLAP AND DATA MINING
261
112 DATA MINING IN DATA WAREHOUSES
262
113 DECISION SUPPORT QUERIES DATA WAREHOUSE AND OLAP
263
1132 ARCHITECTURE OF DATA WAREHOUSES
264
1133 BASICS OF OLAP
266
114 DATA WAREHOUSE AS MATERIALIZED VIEWS AND INDEXING
270
1142 MATERIALIZED VIEWS
271
1143 MAINTENANCE OF MATERIALIZED VIEWS
273
1144 NORMALIZATION AND DENORMALIZATION OF MATERIALIZED VIEWS
274
1145 INDEXING TECHNIQUES FOR IMPLEMENTATION
275
115 REMARKS ON PHYSICAL DESIGN OF DATA WAREHOUSES
277
116 SEMANTIC DIFFERENCES BETWEEN DATA MINING AND OLAP
278
1162 AGGREGATION SEMANTICS
279
117 NONMONOTONIC REASONING IN DATA WAREHOUSING ENVIRONMENT
282
118 COMBINING DATA MINING AND OLAP
283
1182 SOME SPECIFIC ISSUES
284
119 CONCEPTUAL QUERY ANSWERING IN DATA WAREHOUSES
288
1192 REWRITING CONCEPTUAL QUERY USING MATERIALIZED VIEWS
289
1110 WEB MINING
290
11102 DISCOVERY TECHNIQUES ON WEB TRANSACTIONS
291
SUMMARY
293
REASONING UNDER UNCERTAINTY
297
122 GENERAL REMARKS ON UNCERTAIN REASONING
298
1222 DIFFERENT TYPES OF UNCERTAINTY AND ONTOLOGIES OF UNCERTAINTY
299
1223 UNCERTAINTY AND SEARCH
300
123 UNCERTAINTY BASED ON PROBABILITY THEORY
301
1232 BAYESIAN APPROACH
302
1233 BAYESIAN NETWORKS
303
1234 BAYESIAN NETWORK APPROACH FOR DATA MINING
307
1235 A BRIEF REMARK ON INFLUENCE DIAGRAM AND DECISION THEORY
310
1236 PROBABILITY THEORY WITH MEASURED BELIEF AND DISBELIEF
311
124 FUZZY SET THEORY
314
1242 FUZZY SET OPERATIONS
317
1243 RESOLUTION IN POSSIBILISTIC LOGIC
319
125 FUZZY RULES AND FUZZY EXPERT SYSTEMS
320
1252 SYNTAX AND SEMANTICS OF FUZZY RULES
321
1253 FUZZY INFERENCE METHODS
324
126 USING FUZZYCLIPS
326
127 FUZZY CONTROLLERS
328
1272 BUILDING FUZZY CONTROLLER USING FUZZYCLIPS
329
1273 FUZZY CONTROLLER DESIGN PROCESS
332
128 THE NATURE OF FUZZY LOGIC
335
1281 THE INCONSISTENCY OF FUZZY LOGIC
336
1283 IMPLICATION TO MAINSTREAM COMPUTATIONAL INTELLIGENCE
337
SELFEXAMINATION QUESTIONS
338
RE APPROACHES FOR UNCERTAIN REASONING AND DATA MINING
341
1322 RECONSTRUCTION AND DATA MINING
342
133 SOME KEY IDEAS OF KSYSTEMS THEORY AND ROUGH SET THEORY
343
1332 REDUCTIONDRIVEN APPROACH IN ROUGH SET THEORY
344
1333 KSYSTEMS THEORY VERSUS AND ROUGH SET THEORY
345
1342 TERMINOLOGY
346
1343 AN EXAMPLE
347
1344 RULE INDUCTION USING ROUGH SET APPROACH
349
1345 APPLICATIONS OF ROUGH SETS
350
135 KSYSTEMS THEORY
351
SUMMARY
353
SELFEXAMINATION QUESTIONS
354
TOWARD INTEGRATED HEURISTIC DECISION MAKING
357
143 HIGH LEVEL HEURISTICS FOR PROBLEM SOLVING AND DECISION SUPPORT
359
1433 SUMMARY OF HEURISTICS
364
1442 METAKNOWLEDGE AND METAREASONING
366
1443 METAKNOWLEDGE AND METAPATTERNS IN DATA MINING
371
1444 METALEARNING
373
SUMMARY
374
REFERENCES
375
INDEX
377
Copyright

Other editions - View all

Common terms and phrases