SPRINT: A scalable parallel classifier for data mining

SPRINT: A scalable parallel classifier for data mining (1996)

by John Shafer , Rakesh Agrawal , Manish Mehta

Venue:	RESEARCH REPORT, IBM ALMADEN RESEARCH
Citations:	18 - 6 self

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Datum	Value	Source
TITLE	SPRINT: A scalable parallel classifier for data mining	user correction
AUTHOR NAME	John Shafer	user correction
AUTHOR AFFIL	IBM Almaden Research Center	user correction
AUTHOR ADDR	650 Harry Road, San Jose, CA 95120	user correction
AUTHOR NAME	Rakesh Agrawal	user correction
AUTHOR AFFIL	IBM Almaden Research Center	user correction
AUTHOR ADDR	650 Harry Road, San Jose, CA 95120	user correction
AUTHOR NAME	Manish Mehta	user correction
AUTHOR AFFIL	IBM Almaden Research Center	user correction
AUTHOR ADDR	650 Harry Road, San Jose, CA 95120	user correction
ABSTRACT	Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm an ideal tool for data mining.	user correction
YEAR	1996	INFERENCE
VENUE	RESEARCH REPORT, IBM ALMADEN RESEARCH	user correction
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	25 found	ParsCit 1.0