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Mining Collective Outliers I: On the Set of “Structured Objects”

  • Collective outlier if objects as a group deviate significantly from the entire data
  • Need to examine the structure of the data set, i.e, the relationships between multiple data objects
  • Each of these structures is inherent to its respective type of data
    • For temporal data (such as time series and sequences), we explore the structures formed by time, which occur in segments of the time series or subsequences
    • For spatial data, explore local areas
    • For graph and network data, we explore subgraphs
  • Difference from the contextual outlier detection: the structures are often not explicitly defined, and have to be discovered as part of the outlier detection process.
  • Collective outlier detection methods: two categories
    • Reduce the problem to conventional outlier detection
      • Identify structure units, treat each structure unit (e.g., subsequence, time series segment, local area, or subgraph) as a data object, and extract features
      • Then outlier detection on the set of “structured objects” constructed as such using the extracted features

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