Simple Neighborhood Representative Pre-processing Boosts Outlier Detectors

Yang, Jiawei; Chen, Yu

doi:10.48550/arxiv.2010.12061

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, neighborhood averaging [72] can be used as the post-processing component to guarantee that similar objects in the feature space have similar outlier scores. Neighborhood representative [73] and regional ensemble [74] can be used as the ensemble components to combine multiple outlier scores to produce more reliable outlier scores for data objects. The two stages of median absolute deviation (MAD) [75] can be used as the threshold component [75].…”

Section: Motivation and Contributionmentioning

confidence: 99%

MiPo: How to Detect Trajectory Outliers with Tabular Outlier Detectors

2022

View full text Add to dashboard Cite

Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular features from trajectory data prior to the application of conventional outlier detectors to detect trajectory outliers. By representing variable-length trajectory data as fixed-length tabular data, MiPo granted tabular outlier detectors the ability to detect trajectory outliers, which was previously impossible. Experiments with real-world datasets showed that MiPo outperformed all baseline methods with 0.99 AUC on average; however, it only required approximately 10% of the computing time of the existing industrial best. MiPo exhibited linear time and space complexity. The features extracted by MiPo may aid other trajectory data mining tasks. We believe that MiPo has the potential to revolutionize the field of trajectory outlier detection.

show abstract