Spatio-Temporal Outlier Detection in Large Databases

Derya Birant, Alp Kut


Outlier detection is one of the major data mining methods. This paper proposes a three-step approach to detect spatio-temporal outliers in large databases. These steps are clustering, checking spatial neighbors, and checking temporal neighbors. In this paper, we introduce a new outlier detection algorithm to find small groups of data objects that are exceptional when compared with the remaining large amount of data. In contrast to the existing outlier detection algorithms, the new algorithm has the ability of discovering outliers according to the non-spatial, spatial and temporal values of the objects. In order to demonstrate the new algorithm, this paper also presents an example of application using a data warehouse.

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