Temporal Data Mining in Dynamic Feature Spaces

@article{Wenerstrom2006TemporalDM,
  title={Temporal Data Mining in Dynamic Feature Spaces},
  author={Brent Wenerstrom and Christophe G. Giraud-Carrier},
  journal={Sixth International Conference on Data Mining (ICDM'06)},
  year={2006},
  pages={1141-1145}
}
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. 
Highly Cited
This paper has 37 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
21 Extracted Citations
19 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 21 extracted citations

Similar Papers

Loading similar papers…