Sequential update of ADtrees

Authors:
Josep Roure;Andrew W. Moore
Affiliations:
Carnegie Mellon University Pittsburgh, PA;Carnegie Mellon University Pittsburgh, PA
Venue:
ICML '06 Proceedings of the 23rd international conference on Machine learning
Year:
2006

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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.