Outlying subspace detection for high-dimensional data
Edited book (chapter)
Chapter Title | Outlying subspace detection for high-dimensional data |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 2177 |
Book Title | Encyclopedia of database technologies and applications, 2nd ed. |
Authors | Zhang, Ji (Author), Gao, Qigang (Author) and Wang, Hai (Author) |
Editors | Rivero, Laura C., Doorn, Jorge Horacio and Ferraggine, Viviana E. |
Page Range | 1-6 |
Number of Pages | 6 |
Year | 2009 |
Publisher | IGI Global |
Place of Publication | Hershey, PA, United States |
ISBN | 9781591405603 |
Abstract | [Conclusion]: This article formulates the outlying subspace detection problem and provides a survey of the existing methods for solving this problem. In particular, it focuses on the metrics used to measure the outlier quality of given data points in different subspaces and the searching strategies employed by the existing techniques for exploring high-dimensional space lattices. We have also pointed out the major limitations of the existing techniques, and some important issues to be considered in developing a better outlying subspace detection method in future research work. |
Keywords | subspace detection |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Dalhousie University, Canada |
Saint Mary's University, Canada |
https://research.usq.edu.au/item/9z283/outlying-subspace-detection-for-high-dimensional-data
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