Database Mining: A Performance Perspective

Database Mining: A Performance Perspective (1993)

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by Rakesh Agrawal , Tomasz Imielinski , Arun Swami

Venue:	IEEE Transactions on Knowledge and Data Engineering
Citations:	247 - 12 self

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Datum	Value	Source
TITLE	Database Mining: A Performance Perspective	user correction - Legacy Corrections
AUTHOR NAME	Rakesh Agrawal	user correction - Legacy Corrections
AUTHOR AFFIL	IBM Almaden Research Center	user correction - Legacy Corrections
AUTHOR ADDR	650 Harry Road; San Jose, CA 95120-6099	user correction - Legacy Corrections
AUTHOR NAME	Tomasz Imielinski	user correction - Legacy Corrections
AUTHOR AFFIL	IBM Almaden Research Center	user correction - Legacy Corrections
AUTHOR ADDR	650 Harry Road; San Jose, CA 95120-6099	user correction - Legacy Corrections
AUTHOR NAME	Arun Swami	user correction - Legacy Corrections
AUTHOR AFFIL	IBM Almaden Research Center	user correction - Legacy Corrections
AUTHOR ADDR	650 Harry Road; San Jose, CA 95120-6099	user correction - Legacy Corrections
ABSTRACT	We present our perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology. We describe three classes of database mining problems involving classification, associations, and sequences, and argue that these problems can be uniformly viewed as requiring discovery of rules embedded in massive data. We describe a model and some basic operations for the process of rule discovery. We show how the database mining problems we consider map to this model and how they can be solved by using the basic operations we propose. We give an example of an algorithm for classification obtained by combining the basic rule discovery operations. This algorithm not only is efficient in discovering classification rules but also has accuracy comparable to ID3, one of the current best classifiers. Index Terms. database mining, knowledge discovery, classification, associations, sequences, decision trees Current address: Computer Science De...	user correction - Legacy Corrections
YEAR	1993	INFERENCE
VENUE	IEEE Transactions on Knowledge and Data Engineering	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	914--925	INFERENCE
VOLUME	5	INFERENCE
CITATIONS	20 found	ParsCit 1.0