TY - JOUR
T1 - A Novel Outlier Detection Method for Multivariate Data
AU - Almardeny, Yahya
AU - Boujnah, Noureddine
AU - Cleary, Frances
N1 - Publisher Copyright:
IEEE
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Cost function
KW - Data Mining
KW - Euclidean distance
KW - Linearity
KW - Multivariate Data
KW - Outlier Detection
KW - Rotation Based Outliers
KW - Sorting
KW - Three-dimensional displays
KW - Toy manufacturing industry
UR - http://www.scopus.com/inward/record.url?scp=85096831869&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2020.3036524
DO - 10.1109/TKDE.2020.3036524
M3 - Article
AN - SCOPUS:85096831869
SN - 1041-4347
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
ER -