Mining contrast subspaces
Lei Duan, Ganyi Tang, Jianjie Pei, James A Bailey, Dong Gong, Akiko Campbell, Chi-Keung Tang · 2014
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 maxi-mize 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 experi-mental results on real data sets verify the effectiveness and efficiency of our method. 1