Process Mining with Python tutorial: A healthcare application — Part 3

This article is the third of a tutorial series made up of the following parts:

• Part 1: Introduction to process mining, data preprocessing and initial data exploration.
• Part 2: Primer on process discovery using the PM4Py (Python) library to apply the Alpha Miner algorithm.
• Part 3 (this article): Other process discovery algorithms and model representations.
• Part 4: More holistic models which integrate control flow, time (e.g. bottlenecks, wait times), resources (e.g. personnel capacity and performance, inter-personnel relationships, department/ward capacity and performance), case attributes (e.g. patient demographics, clinical condition).

You can find the complete source code and data for this tutorial series here. In Part 1 of the tutorial, you saw how to prepare and explore your data to get a high level ‘feel’ for the processes. In Part 2, you learned how to apply the alpha miner algorithm to discover a process model using the pm4py library. In this part, we will work with the same example dataset as we used in the previous two parts to learn about two further process discover algorithms — heuristic miner and inductive miner.

Choosing between algorithms

Before we look at heuristic miner and inductive miner, it is worth taking a step back and thinking about how we might choose between algorithms. This is something that has…