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Simple Discretization: Binning

  • Equal-width (distance) partitioning
    • Divides the range into N intervals of equal size: uniform grid
    • if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B A)/N.
    • The most straightforward, but outliers may dominate presentation
    • Skewed data is not handled well
  • Equal-depth (frequency) partitioning
    • Divides the range into N intervals, each containing approximately same number of samples
    • Good data scaling
    • Managing categorical attributes can be tricky

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