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.
& Tang, C.
(2014). Mining Contrast Subspaces. Lecture Notes in Computer Science, 8443, 249-260.