A Survey of Methods for Scaling Up

One of the deening challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. By collecting, categorizing, and summarizing existing work on scaling up inductive algorithms, this paper serves to establish common ground for researchers addressing the challenge. We concentrate on algorithms that build decision trees and rule sets, in order to provide focus and speciic 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 diierent constituent techniques, drawing on speciic examples from published papers. Finally, we use the preceding analysis to suggest how one should proceed when dealing with a large problem, and where future research eeorts should be focused.