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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Synchronization Based Outlier Detection
verfasst von : Junming Shao, Christian Böhm, Qinli Yang, Claudia Plant
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
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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.