  The task of outlier detection is to find small groups of data objects that
are exceptional when compared with rest large amount of data. Detection of such
outliers is important for many applications such as fraud detection and
customer migration. Most existing methods are designed for numeric data. They
will encounter problems with real-life applications that contain categorical
data. In this paper, we formally define the problem of outlier detection in
categorical data as an optimization problem from a global viewpoint. Moreover,
we present a local-search heuristic based algorithm for efficiently finding
feasible solutions. Experimental results on real datasets and large synthetic
datasets demonstrate the superiority of our model and algorithm.
