Enhancing Approximate Conformance Checking Accuracy with Hierarchical Clustering Model Behaviour Sampling

Conformance checking techniques evaluate how well a process model aligns with an actual event log. Existing methods, which are based on optimal trace alignment, are computationally intensive. To improve efficiency, a model sampling method has been proposed to construct a subset of model behaviour that represents the entire model. However, current model sampling techniques often lack sufficient model representativeness, limiting their potential to achieve optimal approximation accuracy. This study proposes new model behaviour sampling approaches using hierarchical clustering to compute an approximation closer to the exact result. This study also refines the existing upper bound algorithm for better approximation. Our experiments on six real-world event logs demonstrate that our method improves approximation accuracy compared to state-of-the-art model sampling methods.

@article{Lyu2025,
  title     = {Enhancing Approximate Conformance Checking Accuracy with Hierarchical Clustering Model Behaviour Sampling},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yilin Lyu},
  doi       = {10.14569/IJACSA.2025.0160803},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160803}
}