Discovering outlying aspects in large datasets

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,...

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Veröffentlicht in:	Data mining and knowledge discovery 2016-11, Vol.30 (6), p.1520-1555
Hauptverfasser:	Vinh, Nguyen Xuan, Chan, Jeffrey, Romano, Simone, Bailey, James, Leckie, Christopher, Ramamohanarao, Kotagiri, Pei, Jian
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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