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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
Brent Wenerstrom, Sharp Analytics, USA
Christophe Giraud-Carrier, Brigham Young University, USA
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
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