Akiko Campbell

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When we are investigating an object in a data set, which itself may or may not be an outlier, can we identify unusual (i.e., outlying) aspects of the object? In this paper, we identify the novel problem of mining outlying aspects on numeric data. Given a query object $$o$$ o in a multidimensional numeric data set $$O$$ O , in which subspace is $$o$$ o most(More)
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(More)
Benchmarking is among the most widely adopted practices in business today. However, to the best of our knowledge, conducting multidimensional benchmarking in data warehouses has not been explored from a technical efficiency perspective. In this paper, we formulate benchmark queries in the context of data warehousing and business intelligence, and develop(More)
We tackle the novel problem of mining contrast subspaces. Given a set of multidimensional objects in two classes $$C_+$$ C + and $$C_-$$ C - and a query object $$o$$ o , we want to find the top- $$k$$ k subspaces that maximize the ratio of likelihood of $$o$$ o in $$C_+$$ C + against that in $$C_-$$ C - . Such subspaces are very useful for characterizing an(More)
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