Computational Intelligence For Decision Support / Edition 1

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Overview

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.

"...presents the subject in an integrated approach & the author explains how to use computational intelligence & DBMS together to attain the goal of high-level decision making."

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Editorial Reviews

Booknews
Provides a holistic approach to high-level decision making that integrates artificial intelligence and database management systems (DBMS). Approaches computational intelligence as the science developed for decision support, and treats reasoning as extended retrieval. Offers a self-contained treatment including background material, and provides specific techniques from both worlds needed to support decision making, including those related to data mining and reasoning under uncertainty. Includes numerous examples, and chapter questions. Chen teaches at the University of Nebraska-Omaha. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

DECISION SUPPORT AND COMPUTATIONAL INTELLIGENCE
The Need for Decision Support Agents
Computerized Decision Support Mechanisms
Computational Intelligence for Decision Support
A Remark on Terminology
Data, Information, and Knowledge
Issues to be Discussed in This Book
SEARCH AND REPRESENTATION
Sample Problems and Applications of Computational Intelligence
Definition of Computational Intelligence
Basic Assumptions of Computational Intelligence
Basic Storage and Search Structures
Problem Solving Using Search
Representing Knowledge for Search
State Space Search
Remark on Constraint-Based Search
Planning and Machine Learning as Search
PREDICATE LOGIC
First Order Predicate Logic
Prolog for Computational Intelligence
Abduction and Induction
Nonmonotonic Reasoning
RELATIONS AS PREDICATES
The Concept of Relation
Overview of Relational Data Model
Relational Algebra
Relational Views and Integrity Constraints
Functional Dependencies
Basics of Relational Database Design
Multivalued Dependencies
Remark on Object-Oriented Logical Data Modeling
Basics of Deductive Databases
Knowledge Representation Meets Databases
RETRIEVAL SYSTEMS
Database Management Systems (DBMS)
Commercial Languages for Database Management Systems
Basics of Physical Database Design
An Overview of Query Processing and Transaction Processing.
Information Retrieval (IR)
Data Warehousing
Rule-Based Expert Systems
Knowledge Management and Ontologies
CONCEPTUAL DATA AND KNOWLEDGE MODELING
OVERVIEW
Entity-Relationship Modeling
Remark on Legacy Data Models
Knowledge Modeling for Knowledge Representation
Structured Knowledge Representation
Frame Systems
Conceptual Graphs
User Modeling and Flexible Inference Control
REASONING AS EXTENDED RETRIEVAL
Beyond Exact Retrieval
Reasoning as Query-Invoked Memory Re-Organization
COMPUTATIONAL CREATIVITY AND COMPUTER ASSISTED HUMAN INTELLIGENCE
Computational Aspects of Creativity
Idea Processors
Retrospective Analysis for Scientific Discovery and Technical Invention
Combining Creativity with Expertise
CONCEPTUAL QUERIES AND INTENSIONAL ANSWERING
A Review of Question Answering Systems
Intensional Answering and Conceptual Query
An Approach for Intensional Conceptual Query Answering
FROM MACHINE LEARNING TO DATA MINING
Basics of Machine Learning
Inductive Learning
Efficiency and Effectiveness of Inductive Learning
Other Machine Learning Approaches
Features of Data Mining
Categorizing Data Mining Techniques
Association Rules
DATA WAREHOUSING, OLAP, AND DATA MINING
Data Mining in Data Warehouses
Decision Support Queries, Data Warehouse, and OLAP
Data Warehouse as Materialized Views and Indexing
Remarks on Physical Design of Data Warehouses
Semantic Differences Between Data Mining and OLAP
Nonmonotonic Reasoning in Data Warehouding Environment
Combining Data Mining and OLAP
Conceptual Query Answering Data Warehouses
Web Mining
REASONING UNDER UNCERTAINTY
General Remarks on Uncertain Reasoning
Uncertainty Based on Probability Theory
Fuzzy Set Theory
Fuzzy Rules and Fuzz Expert Systems
Using Fuzzy CLIPS
Fuzzy Controllers
The Nature of Fuzzy Logic
REDUCTION AND RECONSTRUCTION APPROACHES FOR UNCERTAIN REASONING AND DATA MINING
The Reduction-Reconstruction Duality
Some Key Ideas of K-systems Theory and Rough Set Theory
Rough Sets Approach
K-Systems Theory
TOWARD INTEGRATED HEURISTIC DECISION MAKING
Integrated Problem Solving
High-Level Heuristics for Problem Solving and Decision Support
Meta-Issues for Decision Making
Each section also contains an overview, summary, and self-examination questions.

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