1. CHAPTER 16
Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications

2. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications
Several Real-World Applications of ANN Technology
Advanced AI Systems
Genetic Algorithms
Fuzzy Logic
Qualitative Reasoning
Integration (Hybrids)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

3. Areas of ANN Applications: An Overview
Representative Business ANN Applications
Accounting
Finance
Human Resources
Management
Marketing
Operations
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

4. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Accounting
Identify tax fraud
Enhance auditing by finding irregularities
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

5. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Finance
Signatures and bank note verifications
Mortgage underwriting
Foreign exchange rate forecasting
Country risk rating
Bankruptcy prediction
Customer credit scoring
Credit card approval and fraud detection
Stock and commodity selection and trading
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

6. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Finance 2
Credit card profitability
Forecasting economic turning points
Bond rating and trading
Pricing initial public offerings
Loan approvals
Economic and financial forecasting
Risk management
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

7. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Human Resources
Predicting employees’ performance and behavior
Determining personnel resource requirements
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

8. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Management
Corporate merger prediction
Country risk rating
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

9. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Marketing
Consumer spending pattern classification
Customers’ characteristics
Sales forecasts
Data mining
Airline fare management
Direct mail optimization
Targeted marketing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

10. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Operations
Airline crew scheduling
Predicting airline seat demand
Vehicle routing
Assembly and packaged goods inspection
Quality control
Matching jobs to candidates
Production/job scheduling
Factory process control
Many More
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

11. Credit Approval with Neural Networks
Increases loan processor productivity by 25 to 35% over other computerized tools
Also detects credit card fraud
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

12. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
The ANN Method
Data from the application and into a database
Preprocess applications manually
Neural network trained in advance with many good and bad risk cases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

13. Neural Network Credit Authorizer Construction Process
Step 1: Collect data
Step 2: Separate data into training and test sets
Step 3: Transform data into network inputs
Step 4: Select, train, and test network
Step 5: Deploy developed network application
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

14. Bankruptcy Prediction with Neural Networks
Concept Phase
Paradigm: Three-layer network, back-propagation
Training data: Small set of well-known financial ratios
Data available on bankruptcy outcomes
Supervised network
Training time not to be a problem
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

15. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Application Design
Five Input Nodes
X1: Working capital/total assets
X2: Retained earnings/total assets
X3: Earnings before interest and taxes/total assets
X4: Market value of equity/total debt
X5: Sales/total assets
Single Output Node: Final classification for each firm
Bankruptcy or
Nonbankruptcy
Development Tool: NeuroShell
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

16. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Development
Three-layer network with backpropagation (Figure 16.3)
Continuous valued input
Single output node: 0 = bankrupt, 1 = not bankrupt
Training
Data Set: 129 firms
Training Set: 74 firms; 38 bankrupt, 36 not
Ratios computed and stored in input files for:
The neural network
A conventional discriminant analysis program
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

17. Architecture of the Bankruptcy Prediction Neural Network (Figure 16.3)X1 X2
Bankrupt X3
Not bankrupt 1 X4 X5
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

18. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Parameters
Learning threshold
Learning rate
Momentum
Testing
Two Ways
Test data set: 27 bankrupt firms, 28 nonbankrupt firms
Comparison with discriminant analysis
The neural network correctly predicted:
81.5 percent bankrupt cases
82.1 percent nonbankrupt cases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

19. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
ANN did better predicting 22 out of the 27 actual cases
Discriminant analysis predicted only 16 correctly
Error Analysis
Five bankrupt firms misclassified by both methods
Similar for nonbankrupt firms
Neural network at least as good as conventional
Accuracy of about 80 percent is usually acceptable for neural network applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

20. Stock Market Prediction System with Modular Neural Networks
Accurate Stock Market Prediction - Complex Problem
Several Mathematical Models - Disappointing Results
Fujitsu and Nikko Securities: TOPIX Buying and Selling Prediction System
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

21. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Input: Several technical and economic indexes
Several modular neural networks relate past indexes, and buy/sell timing
Prediction system
Modular neural networks
Very accurate
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

22. Network Architecture (Figure 16.4)
Network Model: 3 layers, standard sigmoid function, continuous output [0, 1]
High-speed Supplementary Learning Algorithm
Training Data
Data Selection
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

23. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Preprocessing: Input Indexes - Converted into spatial patterns, preprocessed to regularize them
Moving Simulation Prediction Method (Figure 16.5)
Result of Simulations
Simulation for Buying and Selling Stocks
Example (Figure 16.6)
Excellent Profit
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

24. Integrated ANNs and Expert Systems
1. Resource Requirements Advisor
Advises users on database systems’ resource requirements
Predicts the time and effort to finish a database project
ES shell AUBREY and neural network tool NeuroShell
ES supported data collection
ANN used for data evaluation
ES final analysis
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

25. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
2. Personnel Resource Requirements Advisor
Project personnel resource requirements for maintaining networks or workstations at NASA
Rule-based ES determines the final resource projections
ANN provides project completion times for services requested
(Figure 16.7)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

26. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
3. Diagnostic System for an Airline
Singapore Airlines
Assists technicians in diagnosing avionics equipment
INSIDE (Inertial Navigation System Interactive Diagnostic Expert)
Designed to reduce the diagnostic time
(Figure 16.8)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

27. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
4. Manufacturing Product Liability
United Technologies Carrier
Two ES + ANN
Patterns fed into multilayer feedforward ANN
Integrated with a database into an Automatic Early Warning System (AEWS)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

28. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
5. Oil Refinery Production Scheduling and Environmental Control
Citgo Petroleum Corporation
Lower costs
Improved safety
Higher product quality
Higher yields
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

29. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Genetic Algorithms
Goal (evolutionary algorithms): Demonstrate self-organization and adaptation by exposure to the environment
System learns to adapt to changes.
Example 1: Vector Game
Random trial and error
Genetic algorithm solution
Process (Figure 16.9)
Example: the game of MasterMind
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

30. Genetic Algorithm Definition and Process
Genetic algorithm: "an iterative procedure maintaining a population of structures that are candidate solutions to specific domain challenges” (Grefenstette [1982])
Each candidate solution is called a chromosome
Chromosomes can copy themselves, mate, and mutate
Use specific genetic operators - reproduction, crossover and mutation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

31. Primary Operators of Most Genetic Algorithms
Reproduction
Crossover
Mutation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

32. Genetic Algorithm Operators
Parent 1
Parent 2
Child 1
Mutation
Child 2
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

33. GA Example: The Knapsack Problem
Item:
Benefit:
Weight:
Knapsack holds a maximum of 22 pounds
Fill it to get the maximum benefit
Solutions take the form of a string of 1’s
Solution:
Means choose items 1, 2, 5. Weight = 21, Benefit = 20
Evolver solution in Figure 16.10
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

34. Genetic Algorithms Applications and Software
Type of machine learning
Set of efficient, domain-independent search heuristics for a broad spectrum of applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

35. Genetic Algorithm Application Areas
Dynamic process control
Induction of rule optimization
Discovering new connectivity topologies
Simulating biological models of behavior and evolution
Complex design of engineering structures
Pattern recognition
Scheduling
Transportation
Layout and circuit design
Telecommunication
Graph-based problems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

36. Business Applications
Channel 4 Television (England) to schedule commercials
Driver scheduling in a public transportation system
Jobshop scheduling
Assignment of destinations to sources
Trading stocks
Productivity in whisky-making is increased
Often genetic algorithm hybrids with other AI methods
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

37. Representative Commercial Packages
Evolver (Excel spreadsheet add-in)
Genetic Algorithm User Interface (GAUI)
OOGA (Object-Oriented GA for industrial use)
XperRule Genasys (ES shell with an embedded genetic algorithm)
Sugal Genetic Algorithm Simulator
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

38. Optimization Algorithms
Via neural computing sometimes
Genetic algorithms and their derivatives can optimize (or nearly optimize) complex problems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

39. Decision making involves gray areas and the term maybe
Fuzzy Logic
Fuzzy logic deals with uncertainty
Uses the mathematical theory of fuzzy sets
Simulates the process of normal human reasoning
Allows the computer to behave less precisely and logically
Decision making involves gray areas and the term maybe
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

40. Fuzzy Logic Advantages
Provides flexibility
Provides options
Frees the imagination
More forgiving
Allows for observation
Shortens system development time
Increases the system's maintainability
Uses less expensive hardware
Handles control or decision-making problems not easily defined by mathematical models
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

41. Fuzzy Logic Example: What is Tall?
In-Class Exercise
Proportion
Height Voted for
5’10” 0.05
5’11” 0.10 6’
6’1” 0.15
6’2” 0.10
Jack is 6 feet tall
Probability theory - cumulative probability
There is a 75 percent chance that Jack is tall
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

42. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Fuzzy logic - Jack's degree of membership within the set of tall people is 0.75
We are not completely sure whether he is tall or not
Fuzzy logic - We agree that Jack is more or less tall
Membership Function < Jack, 0.75  Tall >
Knowledge-based system approach: Jack is tall (CF = .75)
Belief functions
Can use fuzzy logic in rule-based systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

43. Membership Functions in Fuzzy Sets (Figure 16.11)
Short
Medium
Tall
1.0
Membership
0.5 64 74 69
Height in inches (1 inch = 2.54 cm)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

44. Fuzzy Logic Applications and Software
Difficult to apply when people provide evidence
Used in consumer products that have sensors
Air conditioners
Cameras
Dishwashers
Microwaves
Toasters
Special software packages
Controls applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

45. Examples of Fuzzy Logic
Example 1: Strategic planning
STRATASSIST - fuzzy expert system that helps small- to medium-sized firms plan strategically for a single product
Example 2: Fuzziness in real estate
Example 3: A fuzzy bond evaluation system
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

46. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Fuzzy Logic Software
Fuzzy Inference Development Environment (FIDE)
Z Search
HyperLogic Corporation demos
Others
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

47. Qualitative Reasoning (QR)
Means of representing and making inferences using general, physical knowledge about the world
QR is a model-based procedure that consequently incorporates deep knowledge about a problem domain
Typical QR Logic
“If you touch a kettle full of boiling water on a stove, you will burn yourself”
“If you throw an object off a building, it will go down”
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

48. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
But
No specific knowledge about boiling temperature, just that it is really hot!
No specific information about the building or object, unless you are the object, or you are trying to catch it
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

49. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Main goal of QR: To represent common sense knowledge about the physical world, and the underlying abstractions used in quantitative models (objects fall)
Given such knowledge and appropriate reasoning methods, an ES could make predictions and diagnoses, and explain the behavior of physical systems qualitatively, even when exact quantitative descriptions are unavailable or intractable
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

50. Qualitative Reasoning
Relevant behavior is modeled
Temporal and spatial qualities in decision making are represented effectively
Applies common sense mathematical rules to variables and functions
There are structure rules and behavior rules
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

51. Some Real-World QR Applications
Nuclear plant fault diagnoses
Business processes
Financial markets
Economic systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

52. Intelligent Systems Integration
Combine
Neural Computing
Expert Systems
Genetic Algorithms
Fuzzy Logic
Example: International investment management--stock selection
Fuzzy Logic and ANN (FuzzyNet) to forecast the expected returns from stocks, cash, bonds, and other assets to determine the optimal allocation of assets
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

53. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Global markets
Integrated network architecture of the system
(Figure 16.12)
Technologies
Expert system (rule-based) for country and stock selection
Neural network for forecasting
Fuzzy logic for assessing factors without reliable data
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

54. FuzzyNet Architecture (Figure 16.12)
Membership Function Generator (MFG)
Fuzzy Information Processor (FIP)
Back-propagation Neural Network (BPN)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

55. Data Mining and Knowledge Discovery in Databases (KDD)
Hidden value in data
Knowledge Discovery in Databases (KDD)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

56. The KDD Process Start with Raw Data and Do
1. Selection to produce target the appropriate data which undergoes
2. Preprocessing to filter the data in preparation for
3. Transformation so that
4. Data Mining can identify patterns that go through
5. Interpretation and Evaluation resulting in knowledge
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

57. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Data Mining
Find kernels of value in raw data ore
Theoretical advances
Knowledge discovery in textual databases
Methods based on statistics, cluster analysis, discriminant analysis, fuzzy logic, genetic algorithms, and neural networks
Ideal for data mining
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

58. AI Methods and Data Mining for Search
Neural Networks
Expert Systems
Rule Induction
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

59. Data Mining Applications Areas
Marketing
Investment
Fraud detection
Manufacturing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

60. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Information Overload
Data mining methods can sift through soft information to identify relationships automatically
Intelligent agents
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

61. Important KDD and Data Mining Challenges
Dealing with larger databases
Working with higher dimensionalities of data
Overfitting--modeling noise rather than data patterns
Assessing statistical significance of results
Working with constantly changing data and knowledge
Continue
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

62. 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Working through missing and noisy data
Determining complex relationships between fields
Making patterns more understandable to humans
Providing better user interaction and prior knowledge about the data
Providing integration with other systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ