Mining Contrast Subspaces

Lei Duan, Guanting Tang, Jian Pei, James Bailey, Guozhu Dong, Akiko Campbell, Changjie Tang

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.