Discovering outlying aspects in large datasets

• Published in: Data mining and knowledge discovery Vol. 30; no. 6; pp. 1520 - 1555
• Main Authors: Vinh, Nguyen Xuan, Chan, Jeffrey, Romano, Simone, Bailey, James, Leckie, Christopher, Ramamohanarao, Kotagiri, Pei, Jian
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2016; Springer Nature B.V
• Subjects: Artificial Intelligence; Cancer; Chemistry and Earth Sciences; Computational efficiency; Computer Science; Data mining; Data Mining and Knowledge Discovery; Datasets; Feature selection; Information Storage and Retrieval; Mathematical models; Multidimensional data; Physics; Query processing; Scoring; Searching; Statistics for Engineering; Subspaces; Outlier explanation; Outlying aspects mining; Subspace selection

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,...