A modern approach to transition analysis and process mining with Markov models: A tutorial with R

TL;DR

This paper introduces Markovian models for sequence data analysis, emphasizing probabilistic transitions over deterministic methods, and provides a comprehensive tutorial on implementing these models in R for process mining.

Contribution

It offers a detailed tutorial on various Markovian models and their application in process mining using R, filling a gap in practical guidance for this approach.

Findings

Effective implementation of Markov models in R for process analysis

Comparison of different Markov model variations for process mining

Guidelines for clustering and visualizing process models

Abstract

This chapter presents an introduction to Markovian modeling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapters, Markovian models are probabilistic models, focusing on the transitions between states instead of studying sequences as a whole. The chapter provides an introduction to this method and differentiates between its most common variations: first-order Markov models, hidden Markov models, mixture Markov models, and mixture hidden Markov models. In addition to a thorough explanation and contextualization within the existing literature, the chapter provides a step-by-step tutorial on how to implement each type of Markovian model using the R package seqHMM. The chaper also provides a complete guide to performing stochastic process mining with Markovian models as well as plotting, comparing and clustering different process models.