Outlier Detection and Trend Detection: Two Sides of the Same Coin

@article{Schubert2015OutlierDA,
  title={Outlier Detection and Trend Detection: Two Sides of the Same Coin},
  author={Erich Schubert and Michael Weiler and A. Zimek},
  journal={2015 IEEE International Conference on Data Mining Workshop (ICDMW)},
  year={2015},
  pages={40-46}
}
Outlier detection is commonly defined as the process of finding unusual, rare observations in a large data set, without prior knowledge of which objects to look for. Trend detection is the task of finding some unexpected change in some quantity, such as the occurrence of certain topics in a textual data stream. Many established outlier detection methods are designed to search for low-density objects in a static data set of vectors in Euclidean space. For trend detection, high volume events are… Expand
12 Citations
Algorithmic Frameworks for the Detection of High-Density Anomalies
  • Ralph Foorthuis
  • Computer Science, Mathematics
  • 2020 IEEE Symposium Series on Computational Intelligence (SSCI)
  • 2020
The Impact of Discretization Method on the Detection of Six Types of Anomalies in Datasets
Advances in Event Detection
  • J. Borges, P. Bozsoky, Simon Sudrich, M. Beigl
  • Computer Science
  • 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
  • 2017
Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression
Multivariate spatial analysis for the identification of criticalities and of the subtended causes in river ecosystems
Event detection in high throughput social media
...
1
2
...

References

SHOWING 1-10 OF 88 REFERENCES
A unifying framework for detecting outliers and change points from time series
Incremental Local Outlier Detection for Data Streams
Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces
Outlier Detection with Uncertain Data
LOF: identifying density-based local outliers
Finding centric local outliers in categorical/numerical spaces
Outlier detection by active learning
Anomaly, event, and fraud detection in large network datasets
Mining top-n local outliers in large databases
...
1
2
3
4
5
...