Model-driven Stochastic Trace Clustering

TL;DR

This paper introduces a stochastic, model-driven trace clustering method that enhances process model interpretability and captures real-world dynamics by optimizing cluster-specific stochastic process models using entropic relevance.

Contribution

The paper presents a novel stochastic process model-based trace clustering approach that improves interpretability and captures execution variability, outperforming existing methods in stochastic coherence.

Findings

Yields superior stochastic coherence and graph simplicity.

Scales linearly with input size, ensuring computational efficiency.

Highlights a trade-off between stochastic coherence and traditional fitness metrics.

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 demonstrate that while our method yields superior stochastic coherence and graph simplicity, traditional fitness metrics reveal a trade-off, highlighting the specific utility of our approach for stochastic process analysis.