Outlying Property Detection with Numerical Attributes
It addresses a gap in outlier detection for numerical data, providing a method to explain outlierness, though it appears incremental as it builds on existing outlier detection concepts.
The paper tackles the problem of detecting outlying properties for numerical attributes, introducing a measure to quantify outlierness and an efficient algorithm to compute it relative to significant, rule-based subsets of data.
The outlying property detection problem is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other database objects. In this paper, we analyze the problem within a context where numerical attributes are taken into account, which represents a relevant case left open in the literature. We introduce a measure to quantify the degree the outlierness of an object, which is associated with the relative likelihood of the value, compared to the to the relative likelihood of other objects in the database. As a major contribution, we present an efficient algorithm to compute the outlierness relative to significant subsets of the data. The latter subsets are characterized in a "rule-based" fashion, and hence the basis for the underlying explanation of the outlierness.