1. Critique of the dirty dozen: 12 years of KDD
Daryl Pregibon
AT&T Shannon Laboratory
KDD2001 San Francisco, CA

2. Summary
There remains tremendous opportunity for data mining on the horizon
To take full advantage of these opportunities some changes are necessary

3. The KDD Community (who we are)AI DB
Stats/ML

4. KDD Activities (what we do)
Theory
Methods
Applications

5. We do too much of e-verything
e-commerce
e-business
e-tailing
e-this
e-that
e-nough already!

6. We focus too much on predictive accuracy
Data mining should be about story telling i.e., understanding and interpretability
Why can’t we strive to have both - highly accurate predictions and interpretability?

7. We don’t do enough of…. Foundations/fundamentals
Is there a Shannon-like theory for capacity in a data mining channel?
We have many ways to quantify the amount of data in a DB (#rows/ #tables/ #bytes) so why can’t we do the same for the amount of information in a DB?

8. Scientific applications
Genomic DBs change the dynamic --- will the KDD community respond?
Automation
We already have more data than anyone could ever look at --- where are the data mining agents? The classibots? The regressibots?
Knowledge Discovery in Data as a process
More than just tactics!
Education
How do we train the data mining generation?