Discovering outlying aspects in large datasets

@article{Nguyen2016DiscoveringOA,
  title={Discovering outlying aspects in large datasets},
  author={Xuan Vinh Nguyen and Jeffrey Chan and Simone Romano and James Bailey and Christopher Leckie and Kotagiri Ramamohanarao and Jian Pei},
  journal={Data Mining and Knowledge Discovery},
  year={2016},
  volume={30},
  pages={1520-1555}
}
We address the problem of outlying aspects mining: given a query object and a reference multidimensional data set, how can we discover what aspects (i.e., subsets of features or subspaces) make the query object most outlying? Outlying aspects mining can be used to explain any data point of interest, which itself might be an inlier or outlier. In this paper, we investigate several open challenges faced by existing outlying aspects mining techniques and propose novel solutions, including (a) how… CONTINUE READING
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