KDnuggets : News : 2008 : n03 : item18 < PREVIOUS | NEXT >


From: Xindong Wu University of Vermont
Date: 18 Jan 2008
Subject: Data Mining in Data Mining: 10 Algorithms and 10 Challenging Problems

In 2005 and 2006, the IEEE International Conference on Data Mining (ICDM) took two initiatives to identify 10 challenging problems and 10 most influential algorithms in data mining. The mining results from these two initiatives are now available at

www.cs.uvm.edu/~icdm/10Problems/index.shtml and


respectively with companion journal articles to describe the details.

The 10 challenging problems are listed below (where the order of the listing does not reflect their level of importance):

  • Developing a Unifying Theory of Data Mining
  • Scaling Up for High Dimensional Data and High Speed Data Streams
  • Mining 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
  • Data Mining for Biological and Environmental Problems
  • Data-Mining-Process Related Problems
  • Security, Privacy and Data Integrity
  • Dealing with Non-static, Unbalanced and Cost-sensitive Data
The top 10 algorithms are as follows: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.

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KDnuggets : News : 2008 : n03 : item18 < PREVIOUS | NEXT >

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