Scalable outlying-inlying aspects discovery via feature ranking

Abstract

In outlying aspects mining, given a query object, we aim to answer the question as to what features make the query most outlying. The most recent works tackle this problem using two different strategies. (i) Feature selection approaches select the features that best distinguish the two classes: the query point vs. the rest of the data. (ii) Score-and-search approaches define an outlyingness score, then search for subspaces in which the query point exhibits the best score. In this paper, we first present an insightful theoretical result connecting the two types of approaches. Second, we present OARank – a hybrid framework that leverages the efficiency of feature selection based approaches and the effectiveness and versatility of score-and-search based methods. Our proposed approach is orders of magnitudes faster than previously proposed score-and-search based approaches while being slightly more effective, making it suitable for mining large data sets.