10 Challenging Problems in Data Mining
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
The identification results were presented at the fifth IEEE International
Conference on Data Mining (ICDM '05).
The 10 challenging problems are listed below (where the order of the
listing does not reflect their level of importance):
- Presentation slides: in PDF.
- A companion article in PDF
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.
- Developing a Unifying Theory of Data Mining
- Scaling Up
for High Dimensional Data and High Speed Data Streams
Sequence Data and Time Series Data
- Mining Complex Knowledge
from Complex Data
- Data Mining in a Network Setting
- Distributed Data Mining and Mining Multi-agent Data
Mining for Biological and Environmental Problems
- Data-Mining-Process Related Problems
- Security, Privacy and
- Dealing with Non-static, Unbalanced and
Qiang Yang and Xindong Wu
This page has been accessed times since November 29, 2006.
Last updated: January 18, 2007.