Synchronization Based Outlier Detection
Abstract
The study of extraordinary observations is of great interest in a large variety of applications, such as criminal activities detection, athlete performance analysis, and rare events or exceptions identification. The question is: how can we naturally flag these outliers in a real complex data set? In this paper, we study outlier detection based on a novel powerful concept: synchronization. The basic idea is to regard each data object as a phase oscillator and simulate its dynamical behavior over time according to an extensive Kuramoto model. During the process towards synchronization, regular objects and outliers exhibit different interaction patterns. Outlier objects are naturally detected by local synchronization factor (LSF). An extensive experimental evaluation on synthetic and real world data demonstrates the benefits of our method.
Cite
Text
Shao et al. "Synchronization Based Outlier Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_16Markdown
[Shao et al. "Synchronization Based Outlier Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/shao2010ecmlpkdd-synchronization/) doi:10.1007/978-3-642-15939-8_16BibTeX
@inproceedings{shao2010ecmlpkdd-synchronization,
title = {{Synchronization Based Outlier Detection}},
author = {Shao, Junming and Böhm, Christian and Yang, Qinli and Plant, Claudia},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {245-260},
doi = {10.1007/978-3-642-15939-8_16},
url = {https://mlanthology.org/ecmlpkdd/2010/shao2010ecmlpkdd-synchronization/}
}