10 Challenging Problems in Data Mining Research

In October 2005, we took an initiative to identify 10 challenging problems in data mining research, by consulting some of the most active 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 guideline as to where the hot problems are located in data mining.

The identification results were presented at the fifth IEEE International Conference on Data Mining (ICDM '05).

• Presentation slides: in PDF.
• A companion article in PDF (reprinted from the following journal):
  Qiang Yang and Xindong Wu (Contributors: Pedro Domingos, Charles Elkan, Johannes Gehrke, Jiawei Han, David Heckerman, Daniel Keim, Jiming Liu, David Madigan, Gregory Piatetsky-Shapiro, Vijay V. Raghavan, Rajeev Rastogi, Salvatore J. Stolfo, Alexander Tuzhilin, and Benjamin W. Wah), 10 Challenging Problems in Data Mining Research, International Journal of Information Technology & Decision Making, Vol. 5, No. 4, 2006, 597-604.

1. Developing a Unifying Theory of Data Mining
2. Scaling Up for High Dimensional Data and High Speed Data 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

Qiang Yang and Xindong Wu