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A wide range of methods have been proposed for detecting different types of outliers in full space and subspaces. However, the interpretability of outliers, that is, explaining in what ways and to what extent an object is an outlier, remains a critical open issue. In this paper, we develop a notion of contextual outliers on categorical data. Intuitively, a contextual outlier is a small group of objects that share strong similarity with a significantly larger reference group of objects on some attributes, but deviate dramatically on some other attributes. We develop a detection algorithm, and conduct experiments to evaluate our approach.

Abstract. In this paper, we tackle a novel problem of mining contrast subspaces. Given a set of multidimensional objects in two classes C+ and C− and a query object o, we want to find top-k subspaces S that maximize the ratio of likelihood of o in C+ against that in C−. We demonstrate that this problem has important applications, and at the same time, is very challenging. It even does not allow polynomial time approximation. We present CSMiner, a mining method with various pruning techniques. CSMiner is substantially faster than the baseline method. Our experimental results on real data sets verify the effectiveness and efficiency of our method.

We tackle the novel problem of mining contrast subspaces. Given a set of multidimensional objects in two classes C + and C − and a query object o, we want to find the top-k subspaces that maximize the ratio of likelihood of o in C + against that in C − . Such subspaces are very useful for characterizing an object and explaining how it differs between two classes. We demonstrate that this problem has important applications, and, at the same time, is very challenging, being MAX SNP-hard. We present CSMiner, a mining method that uses kernel density estimation in conjunction with various pruning techniques. B Lei DuanWe experimentally investigate the performance of CSMiner on a range of data sets, evaluating its efficiency, effectiveness, and stability and demonstrating it is substantially faster than a baseline method.