1. COMP5331 Advanced Topics Prepared by Raymond Wong
Presented by Raymond Wong
COMP5331

2. Topics in Project Presentation
Topics in Lectures
Fundamental topics in data mining
Topics in Project Presentation
Advanced topics in data mining
But, they need “concepts” from fundamental topics
COMP5331

3. Advice for Project Presentation
It is not compulsory to attend
It is good to attend to broaden your horizon
COMP5331

4. Topics Association/Frequent Itemset Clustering
How to represent frequent itemsets?
(e.g., maximal frequent itemsets and regular itemsets)
Clustering
How to perform subspace clustering? (e.g., how to find some interesting subspaces)
COMP5331

5. Topics Classification Data Warehouse
How to train a “group” of classifiers (or ensemble)? (e.g., multi-task learning)
Data Warehouse
How to create the data warehouse over different types of data (e.g., graph data)?
COMP5331

6. Topics Data Streams Web DB
How to perform new data mining tasks over data streams?
Web DB
A hot topic (e.g., name ambiguation)
COMP5331

7. Topics Decision-Making
Pattern-based mining (e.g., skyline and periodic patterns)
COMP5331

8. Other Hot Topics Social Network Privacy Issues
How people are connected (e.g., facebook)?
Privacy Issues
How to protect privacy for data mining?
COMP5331

9. Other Hot Topics Graph Data Deep Learning
How to analyze the graph data in a meaning way?
Deep Learning
How to use deep learning in different applications?
COMP5331

10. Final Issues 3 Assignments 1 Project Enough? Difficult?
With a lot of in-class exercises.
1 Project
Difficult?
Depends on you!
COMP5331

11. Final Issues
In-class Participation
Enough?
Please continue!
COMP5331

12. One final issue
Work hard for your exam and your project! 
COMP5331