Detecting outlying subspaces for high-dimensional data: the new task, algorithms and performance
Article
Article Title | Detecting outlying subspaces for high-dimensional data: the new task, algorithms and performance |
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ERA Journal ID | 18060 |
Article Category | Article |
Authors | Zhang, Ji (Author) and Wang, Hai (Author) |
Journal Title | Knowledge and Information Systems |
Journal Citation | 10 (3), pp. 333-355 |
Year | 2006 |
Place of Publication | London, United Kingdom |
ISSN | 0219-1377 |
0219-3116 | |
Web Address (URL) | http://www.springerlink.com/content/x5153138n2r3/?p=35b6418ceb544734afde005b9d9ed794&pi=31 |
Abstract | [Abstract]: In this paper, we identify a new task for studying the outlying degree (OD) of high-dimensional data, i.e. finding the subspaces (subsets of features) |
Keywords | outlying subspace; high-dimensional data; outlier detection; dynamic subspace search |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Dalhousie University, Canada |
Saint Mary's University, Canada |
https://research.usq.edu.au/item/9z29v/detecting-outlying-subspaces-for-high-dimensional-data-the-new-task-algorithms-and-performance
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