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Conference Proceeding

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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.


Presented at the 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Tainan, Taiwan, May 13-16, 2014.

The attached PDF document is the unpublished, peer-reviewed version of this article. The final version of this article can be found at