1. Classification in Complex Systems
Why we should look at the paper:
CAEP: Classification by Aggregating Emerging Patterns
G. Dong, X. Zhang, L. Wong, and J Li

2. What are Common Problems in Classification?
Many variables
Graphs that relate tuples
Protein-protein interactions (KDD-cup 02)
Citations (KDD-cup 03)
Anything that violates standard table format

3. Many Variables Solution: Naïve Bayes way of multiplying probabilities
Other additive models
Problems:
Many factors
May be correlated
Noise
… but it gets worse

4. Graphs 2 kinds of attributes How do neighbor attributes count?
Attributes within nodes
Attributes of neighbor and more distant nodes
How do neighbor attributes count?
Take disjunction?
“At least one neighbor that has a particular property”
Probably preferable:
Use links or, more general, paths as basis
Integration into classification???

5. Idea Get away from strict set of n attributes
If an attribute or combination of attributes is “interesting” use them
Combining rules?
I would have guessed as in Naïve Bayes
CAEP adds probabilities!?

6. What is “interesting” CAEP paper claims “growth rate”
Support of a rule increases significantly from one class label to another
Note: Only increase, not decrease!
What does that mean?
For pattern e and classes P and N
growth_ratePN (e)
= suppN (e) / suppP (e)

7. 2 Things Worth Investigating
Is “interestingness” measure related to information gain?
Under certain assumptions: Yes
Can the “score” be justified?
Sum of P(C)!?

8. Other Issues Normalization How to mine for EPs
Emerging patterns only consider increase in support => different number of relevant patterns
How to mine for EPs

9. Conclusions Idea very valuable Justification of details?
Classification split into ARM-step and rule combination
Justification of details?
Not great
Should be possible to do it right – with poorer accuracy ;-)