A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets

Talk Information

Stanford University, CA, 6/30/00

Paul Komarek


AD-trees, Association Rules, Bayesian Networks, Cached Sufficient Statistics, Decision Trees


This talk was presented at ICML 2000. It describes a modification to AD-trees to allow incremental and lazy growth. We discuss our implementation of these Dynamic AD-trees and present results for datasets with scores of high-arity attributes and millions of rows. ICML 2000.

These slides are best understood with the help of my notes from the presentation. These notes are available (linked) below.

Attachment Data

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