A Survey of Methods for Scaling Up Inductive Algorithms

Abstract. One of the de ning 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 speci c 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 di erent constituent techniques, drawing on speci c 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.

• Type: Journal
• Year: 1999
• Where: DATAMINE
• Authors: Foster J. Provost, Venkateswarlu Kolluri
• Keywords: DATAMINE 1999, Inductive Algorithms, Inductive Learning Algorithms, KDD Research Community