Sequential update of ADtrees

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.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Josep Roure, Andrew W. Moore
• Keywords: Data-mining Algorithms, Efficient Method, ICML 2006, Incremental Environments, Machine Learning