1. LEARNING FROM DATA Lecture Ten (Chapter 10, Notes;
Chapter 11, Textbook)

2. Outline The Concept of Learning Data Visualization Neural Networks
The Basics
Supervised and Unsupervised Learning
Business Applications
Association Rules
Implications for Knowledge Management

3. The “Context” of Learning
The “value added” collaborative intelligence layer of KM architecture
Relevant technologies are:
Artificial Intelligence
Experts Systems
Case-Based Reasoning
Data Warehousing
Intelligent agents
Neural Networks

4. The “Process” of Learning
A process of filtering and transforming data into valid and useful knowledge.
Automate via technology tools:
provide a collaborative learning environment for participants
enhance their ability to understand the processes / tasks they are dealing with

5. The “Goals” of Learning
Final goal is to improve the qualities of communication and decision making
Ways to achieve these goals:
Verify hypotheses (formed from accumulated knowledge)
Discover new patterns in data
Predict future trends and behaviour

6. Learning from Data
Build learning models that automatically improve with experience.
Top-down approach
Generate ideas
Develop models
Validate models
Bottom-up approach
Discover new (unknown) patterns
Find key relationships in data

7. Top-down approach (Example)
Start with a hypothesis derived from observation or prior knowledge
“Tourists visiting Egypt earn an annual income of at least $50,000”
Hypothesis tested by querying database followed by analysis
If tests not supportive, hypothesis is revised and test again

8. Bottom-up approach (Example)
No hypothesis to test
“Find unknown buying patterns by analyzing the shopping basket”
“ … showed married males, age 21 to 27, shopped for diapers also brought beer.
“store decided to stack beer cases next to diaper shelf”

9. Data Visualization
Explore visually for trends in data useful for making decision
Exploring data includes:
Identify key attributes and their distribution
Identify outliers
Extract interesting grouping of data subsets
Identify initial hypothesis

10. Example of Data Visualization (John Snow and the Cholera outbreak in London, 1845)

11. Artificial Neural Network as Learning Model
Modeled after human brain’s network
Simulate biological information processing via networks of interconnected neurons
Neural networks are analog and parallel

12. Neurons – The Basic Elements
The neuron receives inputs, determines their weights (strengths), sums the combined inputs, and compares the total to a threshold (transfer function)
If total is greater than threshold, the neuron fires (sends an output)

13. A Neuron Model

14. Learning in Neural Network
Supervised
The NN needs a teacher with a training set of examples of input and output
Unsupervised (or Self-Supervised)
Does not need a teacher

15. Supervised Learning
Each element in a training set is paired with an acceptable response
Network makes successive passes through the examples
The weights adjust toward the goal state.

16. A Supervised Neural Network (An Example)

17. Unsupervised Learning
No external factors can influence adjustment of input’s weights
No advanced indication of correct or incorrect answers
Adjusts through direct confrontation with new experiences

18. Business Applications (1)
Risk management
Appraise commercial loan applications
NN trained on thousands of applications, half of which were approved and the other half rejected by the bank’s loan officers
Through supervised learning, NN learned to pick risks that constitute a bad loan
Identifies loan applicants who are likely to default on their payments

19. Business Applications (2)
Predicting Foreign Exchange Fluctuations:
A set of relevant indicators were identified, used as inputs to NN
NN was trained for exchange rates of US dollar against Swiss franc and Japanese yen, using data from first 6 months of Then it was tested over an 8- to 11-week period
Results revealed return on capital of about 20%

20. Business Applications (3)
Mortgage Appraisals:
Neural network uses the data in the mortgage loan application
It estimates value of the property based on the immediate neighborhood, the city, and the country
The system comes up with a valuation for the property and a risk analysis for the loan.

21. Association Rules
A KB tool that generates a set of rules to help understanding relationships that exist in data
Types:
Boolean rule
Quantitative rule
Multi-dimensional rule
Multi-level association rule

22. Boolean Rule (An Example)
A rule that examines the presence or absence of items
For example, if a customer buys a PC and a 17” monitor, then he will buy a printer. Presence of items (a PC and 17” monitor) implies presence of the printer in the customer’s buying list

23. Quantitative Rule (An Example)
A rule that considers the quantitative values of items
For example, if a customer earns between $30,000 and $50,000 and owns an apartment worth between $250,000 and $500,000, he will buy a 4-door automobile

24. Multi-dimensional Rule
A rule that refers to a multitude of dimensions
If a customer lives in a big city and earns more than $35,000, then he will buy a cellular phone
This rule involves 3 attributes: living, earning, and buying. Therefore, it is a multi-dimensional rule

25. Multi-level Association Rule

26. Implications for Knowledge Management
Cost / benefit analysis
Tangible costs - user training, hardware + software, backup, support, maintenance
Intangible costs - user resistance and learning curve
Quality Assurance
Adequacy of initial design
Level and frequency of maintenance

27. Layers of KM Architecture
User Interface
(Web browser software installed on each user’s PC) 1 2 3 4 5 6 7
Authorized access control
(e.g., security, passwords, firewalls, authentication)
Collaborative intelligence and filtering
(intelligent agents, network mining, customization, personalization)
Knowledge-enabling applications
(customized applications, skills directories, videoconferencing, decision support systems,
group decision support systems tools)
Transport
( , Internet/Web site, TCP/IP protocol to manage traffic flow)
Middleware
(specialized software for network management, security, etc.)
Then, the blueprint should consider how the functions of the KMS can be placed into a KM architecture, consisting of 7 important layers as shown here.
Details of these layers in the KM architecture will be explained in the next lecture.
The Physical Layer
(repositories, cables)
Data warehousing
(data cleansing,
data mining)
Databases
Legacy applications
(e.g., payroll)
Groupware
(document exchange,
collaboration)