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HiCS: High Contrast Subspaces for Density-Based Outlier Ranking.

, , and . ICDE, page 1037-1048. IEEE Computer Society, (2012)

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Unsupervised interaction-preserving discretization of multivariate data., , , and . Data Min. Knowl. Discov., 28 (5-6): 1366-1397 (2014)DUSC: Dimensionality Unbiased Subspace Clustering., , , and . ICDM, page 409-414. IEEE Computer Society, (2007)Identifying User Interests within the Data Space - a Case Study with SkyServer., , , , , and . EDBT, page 641-652. OpenProceedings.org, (2015)DensEst: Density Estimation for Data Mining in High Dimensional Spaces., , , , and . SDM, page 175-186. SIAM, (2009)Flexible Subspace Search for Outlier Detection and Description.. LWA, page 121. Universitätsbibliothek Bamberg, (2013)Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data., , , , and . ICDM, page 377-386. IEEE Computer Society, (2009)Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data., , , and . ICDM, page 937-942. IEEE Computer Society, (2013)Outlier Ranking via Subspace Analysis in Multiple Views of the Data., , , , and . ICDM, page 529-538. IEEE Computer Society, (2012)Flexible Fault Tolerant Subspace Clustering for Data with Missing Values., , , and . ICDM, page 231-240. IEEE Computer Society, (2011)Spectral Graph Complexity., , , , and . WWW (Companion Volume), page 308-309. ACM, (2019)