A Survey of Methods for Scaling Up Inductive Learning Algorithms

Each year, one of the explicit challenges for the KDD research community is to develop methods that facilitate the use of inductive learning algorithms for mining very large databases. By collecting, categorizing, and summarizing past work on scaling up inductive learning algorithms, this paper serves to establish a common ground for researchers addressing the challenge. We begin with a discussion of important, but often tacit, issues related to scaling up learning algorithms. We highlight similarities among methods by categorizing them into three main approaches. For each approach, we then describe, compare, and contrast the di erent constituent methods, drawing on speci c examples from the published literature. Finally, we use the preceding analysis to suggest how one should proceed when dealing with a large problem, and where future research e orts should be focused. Primary contact: Foster Provost NYNEX Science and Technology, 400 Westchester Avenue, White Plains, NY 10604 email: [email protected] Phone: 914 644 2169 fax: 914 949 9566