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A Survey of Methods for Scaling Up Inductive Algorithms (1999)
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| Venue: | Data Mining and Knowledge Discovery |
| Citations: | 108 - 11 self |
BibTeX
@ARTICLE{Provost99asurvey,
author = {Foster Provost and Venkateswarlu Kolluri},
title = {A Survey of Methods for Scaling Up Inductive Algorithms},
journal = {Data Mining and Knowledge Discovery},
year = {1999},
volume = {3},
pages = {131--169}
}
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Abstract
. One of the defining challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, and compares existing work on scaling up inductive algorithms. We concentrate on algorithms that build decision trees and rule sets, in order to provide focus and specific details; the issues and techniques generalize to other types of data mining. We begin with a discussion of important issues related to scaling up. We highlight similarities among scaling techniques by categorizing them into three main approaches. For each approach, we then describe, compare, and contrast the different constituent techniques, drawing on specific examples from published papers. Finally, we use the preceding analysis to suggest how to proceed when dealing with a large problem, and where to focus future research. Keywords: scaling up, inductive learning, decision trees, rule learning 1. Introduction The knowledge discovery and data...
Keyphrases
inductive algorithm large problem inductive learning different constituent technique rule set decision tree build decision tree important issue kdd research community main approach knowledge discovery specific example future research defining challenge data mining specific detail large database
