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11

ICML
2006
IEEE

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Sequential update of ADtrees

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Sequential update of ADtrees

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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.

Josep Roure, Andrew W. Moore

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Data-mining Algorithms | Efficient Method | ICML 2006 | Incremental Environments | Machine Learning |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2006
Where	ICML
Authors	Josep Roure, Andrew W. Moore

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