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.
This work was supported in part by an NSERC Discovery grant, a BCIC NRAS Team Project, NSFC 61103042, SRFDP 20100181120029, and SKLSE2012-09-32. Work by Lei Duan and Guozhu Dong at Simon Fraser University was supported by an Ebco/Eppich visiting professorship. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
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Duan, L. et al. (2014). Mining Contrast Subspaces. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_21
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DOI: https://doi.org/10.1007/978-3-319-06608-0_21
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