Model-driven Stochastic Trace Clustering

• URL: http://arxiv.org/abs/2506.23776v1
• Date: Mon, 30 Jun 2025 12:18:26 GMT
• Title: Model-driven Stochastic Trace Clustering
• Authors: Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt,
• Abstract summary: Process discovery algorithms automatically extract process models from event logs.<n>High variability often results in complex and hard-to-understand models.<n>Model-driven trace clustering improves on this by assigning traces to clusters based on their conformity to cluster-specific process models.
• Score: 5.373182035720355
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Process discovery algorithms automatically extract process models from event logs, but high variability often results in complex and hard-to-understand models. To mitigate this issue, trace clustering techniques group process executions into clusters, each represented by a simpler and more understandable process model. Model-driven trace clustering improves on this by assigning traces to clusters based on their conformity to cluster-specific process models. However, most existing clustering techniques rely on either no process model discovery, or non-stochastic models, neglecting the frequency or probability of activities and transitions, thereby limiting their capability to capture real-world execution dynamics. We propose a novel model-driven trace clustering method that optimizes stochastic process models within each cluster. Our approach uses entropic relevance, a stochastic conformance metric based on directly-follows probabilities, to guide trace assignment. This allows clustering decisions to consider both structural alignment with a cluster's process model and the likelihood that a trace originates from a given stochastic process model. The method is computationally efficient, scales linearly with input size, and improves model interpretability by producing clusters with clearer control-flow patterns. Extensive experiments on public real-life datasets show that our method outperforms existing alternatives in representing process behavior and reveals how clustering performance rankings can shift when stochasticity is considered.

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