Process mining – Introduction 2

  • Case ID
  • Activity Name
  • Time Stamp

Play out: A possible scenario

Play in: simple process allowing for 4 traces

Replay

Process mining:

  1. Discovery
  2. Conformance
  3. Enhancement

Machine learning:

  1. Supervised learning: response variable that labels each instance (we labeled each data and the machine will learn from that)
    1. Classification: classify to predict (i.e. decision tree)
    2. Regression: final function that fits data
  2. Unsupervised learning: unlabeled. (i.e. clustering like K-means, pattern discovery)

Example: smoker, drinker, weight: supervised learning

Smoker, drinker: predictor variable

Weight: response variable

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