Sixth IEEE International Conference on Data Mining (ICDM'06) Temporal Data Mining in Dynamic Feature Spaces Hong Kong December 18-December 22 ISBN: 0-7695-2701-9
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.157
Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done in this area. This paper presents FAE, an incremental ensemble approach to mining data subject to such concept drift. Empirical results on large data streams demonstrate promise.
Citation:
Brent Wenerstrom, Christophe Giraud-Carrier, "Temporal Data Mining in Dynamic Feature Spaces," icdm, pp.1141-1145, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||