Discovering Interesting Holes in Data

Abstract

Current machine learning and discovery techniques focus on discovering rules or regularities that exist in data. An important aspect of the research that has been ignored in the past is the learning or discovering of interesting holes in the database. If we view each case in the database as a point in a k-dimensional space, then a hole is simply a region in the space that contains no data point. Clearly, not every hole is interesting. Some holes are obvious because it is known that certain value combinations are not possible. Some holes exist because there are insufficient cases in the database. However, in some situations, empty regions do carry important information . For instance, they could warn us about some missing value combinations that are either not known before or are unexpected. Knowing these missing value combinations may lead to significant discoveries. In this paper, we propose an algorithm to discover holes in databases.