Mining Complex Models from Arbitrarily Large Databases in Constant Time

Mining Complex Models from Arbitrarily Large Databases in Constant Time (2002)

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by Geoff Hulten , Pedro Domingos

Venue:	In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Citations:	26 - 10 self

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Datum	Value	Source
TITLE	Mining complex models from arbitrarily large databases in constant time	INFERENCE
AUTHOR NAME	Geoff Hulten	SVM HeaderParse 0.1
AUTHOR AFFIL	; Dept. of Computer Science & Engineering; University of; Categories and Subject Descriptors	SVM HeaderParse 0.2
AUTHOR ADDR	; Washington, Box 352350; Seattle, WA 98195-2350, U.S.A.	SVM HeaderParse 0.1
ABSTRACT	In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm is that its running time becomes independent of the size of the database, while the decisions made are essentially identical to those that would be made given infinite data. The method works within pre-specified memory limits and, as long as the data is iid, only requires accessing it sequentially. It gives anytime results, and can be used to produce batch, stream, time-changing and active-learning versions of an algorithm. We apply the method to learning Bayesian networks, developing an algorithm that is faster than previous ones by orders of magnitude, while achieving essentially the same predictive performance. We observe these gains on a series of large databases generated from benchmark networks, on the KDD Cup 2000 e-commerce data, and on a Web log containing 100 million requests.	SVM HeaderParse 0.1
YEAR	2002	INFERENCE
VENUE	In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	525--531	INFERENCE
VOLUME	8	INFERENCE
CITATIONS	17 found	ParsCit 1.0