Features

Qiang Yang and Xindong Wu

Since ICDM '05, there is a new feature at the IEEE International Conference on Data Mining (ICDM) to identify and present 10 challenging problems in data mining research. We took the initiative this year to consult some of the best-known researchers in data mining and machine learning for their opinions on what are considered important and worthy topics for future research in data mining. We hope their insights will inspire new research efforts, and give young researchers (including PhD students) a high-level guidance as to where the hot problems are located in data mining.

The 10 most challenging problems presented at ICDM '05 are as follows:

1. Developing a Unifying Theory of Data Mining
2. Scaling Up for High Dimensional Data/High Speed Streams
3. Mining Sequence Data and Time Series Data
4. Mining Complex Knowledge from Complex Data
5. Data Mining in a Network Setting
6. Distributed Data Mining and Mining Multi-agent Data
7. Data Mining for Biological and Environmental Problems
8. Data-Mining-Process Related Problems
9. Security, Privacy and Data Integrity
10. Dealing with Non-static, Unbalanced and Cost-sensitive Data

The presentation slides with more details are available on the Web at http://www.cs.uvm.edu/~icdm/