Abstract
As we know that simply applying existing techniques in process mining will often yield a highly incomprehensible process model that called the spaghetti-like model, because real-life processes are typically less structured and more complex than expected by stakeholders. In order to address this issue, trace clustering is considered one of the most relevant pre-processing approaches as grouping similar event logs can radically reduce the complexity of the discovered models. Trace variants denote unique control-flow complete trajectories of a process model. The comparison of trace variants opens the door for a fine-grained analysis of the distinctive features inherent in the execution of a process. In this paper, we propose a split-merge clustering method based on trace variants for pre-processing event logs. Our method consists of three phases: (1) trace variants are filtered out from the event log, and the k-nearest neighbor graph is constructed based on all trace variants; (2) the graph would be partitioned into the initial sub-clusters by applying the coarsening and partitioning operations; (3) we dynamically merge two sub-clusters in the hierarchical clustering process with the relative inter-connectivity and the relative closeness. The experiments on real-life event logs confirmed the improvements of our method compared with the baselines.
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Acknowledgements
The work was supported by the general project numbered KM202310028003 of Beijing Municipal Education Commission, the National Natural Science Foundation of China (61872252), the National Natural Science Foundation of China under Grant 62362067, Yunnan Xing Dian Talents Support Plan.
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Lin, L., Di, Y., Chen, W., Cao, Y., Zhu, R., Zhang, Y. (2023). TCTV: Trace Clustering Considering Intra- and Inter-cluster Similarity Based on Trace Variants. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14420. Springer, Cham. https://doi.org/10.1007/978-3-031-48424-7_6
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