TABLE OF CONTENTS Gary M. Weiss
Department of Computer and Information Science
Fordham University
441 East Fordham Road
Bronx, NY 10458
gweiss@cis.fordham.edu
| Abstract | Rare cases are often the most interesting cases. For example, in medical diagnosis one is typically interested in identifying relatively rare diseases, such as cancer, rather than more frequently occurring ones, such as the common cold. In this chapter we discuss the role of rare cases in Data Mining. Specific problems associated with mining rare cases are discussed, followed by a description of methods for addressing these problems. |
| Keywords: | Rare cases, small disjuncts, inductive bias, sampling |
Rare cases are often of special interest. This is especially true in the context of Data Mining, where one often wants to uncover subtle patterns that may be hidden in massive amounts of data. Examples of mining rare cases include learning word pronunciations (Van den Bosch et al., 1997), detecting oil spills from satellite images (Kubat et al., 1998), predicting telecommunication equipment failures (Weiss and Hirsh, 1998) and finding associations between infrequently purchased supermarket items (Liu et al., 1999). Rare cases warrant special attention because they pose significant problems for Data Mining algorithms.
We begin by discussing what is meant by a rare case. Informally, a case corresponds to a region in the instance space that is meaningful with respect to the domain under study and a rare case is a case that covers a small region of the instance space and covers relatively few training examples. As a...
