Current Slide

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

Mining Contextual Outliers II: Modeling Normal Behavior with Respect to Contexts

  • In some applications, one cannot clearly partition the data into contexts
    • Ex. if a customer suddenly purchased a product that is unrelated to those she recently browsed, it is unclear how many products browsed earlier should be considered as the context
  • Model the “normal” behavior with respect to contexts
    • Using a training data set, train a model that predicts the expected behavior attribute values with respect to the contextual attribute values
    • An object is a contextual outlier if its behavior attribute values significantly deviate from the values predicted by the model
  • Using a prediction model that links the contexts and behavior, these methods avoid the explicit identification of specific contexts
  • Methods: A number of classification and prediction techniques can be used to build such models, such as regression, Markov Models, and Finite State Automaton

Speaker notes:

Content Tools


There are currently no sources for this slide.