A Novel Outlier Detection Method for Multivariate Data

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 (Submitted)

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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
Departments or Groups: Walton Institute for Information and Communications Systems Science
Divisions: School of Business > Department of Graduate Business Studies
Depositing User: Daniel Martins
Date Deposited: 12 Feb 2021 14:37
Last Modified: 12 Feb 2021 14:38
URI: https://repository.wit.ie/id/eprint/3475

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