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Abstract
Outlier detection has important applications in the field of data mining, such as fraud detection, customer behavior analysis, and intrusion detection. Outlier detection is the process of detecting the data objects which are grossly different from or inconsistent with the remaining set of data. Outliers are traditionally considered as single points; however, there is a key observation that many abnormal events have both temporal and spatial locality, which might form small clusters that also need to be deemed as outliers. In other words, not only a single point but also a small cluster can probably be an outlier. In this paper, we present a new definition for outliers: cluster-based outlier, which is meaningful and provides importance to the local data behavior, and how to detect outliers by the clustering algorithm LDBSCAN (Duan et al. in Inf. Syst. 32(7):978-986, 2007) which is capable of finding clusters and assigning LOF (Breunig et al. in Proceedings of the 2000 ACM SIG MOD International Conference on Manegement of Data, ACM Press, pp. 93-104, 2000) to single points. [PUBLICATION ABSTRACT]