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

A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets (2000)

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by Paul Komarek , Andrew Moore

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Datum	Value	Source
TITLE	A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets	user correction - Legacy Corrections
AUTHOR NAME	Paul Komarek	user correction - Legacy Corrections
AUTHOR AFFIL	; Department of Mathematical Sciences, Carnegie Mellon University; School of Computer Science, Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	; 5000 Forbes Ave., Pittsburgh, PA 15213 USA; 5000 Forbes Ave., Pittsburgh, PA 15213 USA; a 2 =3	user correction - Legacy Corrections
AUTHOR NAME	Andrew Moore	user correction - Legacy Corrections
ABSTRACT	This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced with data sets with massive numbers of records or attributes. It briefly reviews the static AD-tree structure of Moore and Lee (1998), and offers a new structure with more attractive properties: (1) the new structure scales better with the number of attributes in the data set; (2) it has zero initial build time; (3) it adaptively caches only statistics relevant to the current task; and (4) it can be used incrementally in cases where new data is frequently being appended to the data set. We provide a careful explanation of the data structure, and then empirically evaluate the performance under varying access patterns induced by different learning algorithms such as association rules, decision trees and Bayes net structures. We conclude by discussing the longer term benefits of the new structure...	user correction - Legacy Corrections
YEAR	2000	user correction - Legacy Corrections
CITATIONS	8 found	ParsCit 1.0