ML Anthology

Sequential Update of ADtrees

Josep Roure, Andrew W. Moore
ICML 2006 pp. 769-776
doi:10.1145/1143844.1143941 /icml/2006/roure2006icml-sequential/

Abstract

Ingcreasingly, data-mining algorithms must deal with databases that continuously grow over time. These algorithms must avoid repeatedly scanning their databases. When database attributes are symbolic, ADtrees have already shown to be efficient structures to store sufficient statistics in main memory and to accelerate the mining process in batch environments. Here we present an efficient method to sequentially update ADtrees that is suitable for incremental environments.

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Text

Roure and Moore. "Sequential Update of ADtrees." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143941

Markdown

[Roure and Moore. "Sequential Update of ADtrees." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/roure2006icml-sequential/) doi:10.1145/1143844.1143941

BibTeX

@inproceedings{roure2006icml-sequential,
  title     = {{Sequential Update of ADtrees}},
  author    = {Roure, Josep and Moore, Andrew W.},
  booktitle = {International Conference on Machine Learning},
  year      = {2006},
  pages     = {769-776},
  doi       = {10.1145/1143844.1143941},
  url       = {https://mlanthology.org/icml/2006/roure2006icml-sequential/}
}