Cluster-based outlier detection

  • Duan L
  • Xu L
  • Liu Y
 et al. 
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Abstract

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.

Author-supplied keywords

  • Cluster-based outlier
  • LDBSCAN
  • Local outlier factor
  • Outlier detection

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Authors

  • Lian Duan

  • Lida Xu

  • Ying Liu

  • Jun Lee

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