Almardeny, Yahya and Boujnah, Noureddine and Cleary, Frances (2020) A Novel Outlier Detection Method for Multivariate Data. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347 (In Press)
Full text not available from this repository. (Request a copy)Abstract
Detecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions. This paper proposes a novel, yet effective outlier learning algorithm that is based on decomposing the full attributes space into different combinations of subspaces, in which the 3D-vectors, representing the data points per 3D-subspace, are rotated about the geometric median, using Rodrigues rotation formula, to construct the overall outlying score. The proposed approach is parameter-free, requires no distribution assumptions and easy to implement. Extensive experimental study and comparison are conducted on both synthetic and real-world datasets with six popular outlier detection algorithms, each from different category. The comparison is evaluated based on the precision @s, average precision, rank power, AUC ROC and time complexity metrics. The results show that the performance of the proposed method is competitive and promising.
Item Type: | Article |
---|---|
Additional Information: | Publisher Copyright: IEEE |
Uncontrolled Keywords: | /dk/atira/pure/subjectarea/asjc/1700/1710 |
Departments or Groups: | |
Depositing User: | Admin SSL |
Date Deposited: | 17 Oct 2022 16:06 |
Last Modified: | 16 Aug 2023 23:01 |
URI: | http://repository-testing.wit.ie/id/eprint/3546 |
Actions (login required)
View Item |