Prototype Selection Using Clustering and Conformance Metrics for Process Discovery

Sani, Mohammadreza Fani; Boltenhagen, Mathilde; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-66498-5_21

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…Clustering event logs can be seen as a preprocessing step to identify sublogs, thus improving the quality of downstream tasks, such as process discovery [17]. Other common encoding methods applied are bags of activities [18], dependency spaces [19], and log footprints [10], for instance.…”

Section: Related Workmentioning

confidence: 99%

Automated Trace Clustering Pipeline Synthesis in Process Mining

Grigore,

Tavares,

Silva

et al. 2024

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Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs.

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Section: Related Workmentioning

confidence: 99%

Automated Trace Clustering Pipeline Synthesis in Process Mining

Grigore,

Tavares,

Silva

et al. 2024

Information

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“…III. Those models have been discovered from the prototypes of the logs defined by [25] and by using the inductive miner (IM) [22] and the split miner (SM) [21] 3 . We recall that the previous SAT implementation of anti-alignment could not deal with entire large logs due to the complexity of the encoding.…”

Section: Precision Of Real-life Modelsmentioning

confidence: 99%

An A^*-Algorithm for Computing Discounted Anti-Alignments in Process Mining

Boltenhagen

Chatain

Carmona

2021

2021 3rd International Conference on Process Mining (ICPM)

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Process mining techniques aim at analyzing and monitoring processes through event data. Formal models like Petri nets serve as an effective representation of the processes. A central question in the field is to assess the conformance of a process model with respect to the real process executions. The notion of anti-alignment, which represents a model run that is as distant as possible to the process executions, has been demonstrated to be crucial to measure precision of models. However, the only known algorithm for computing anti-alignments has a high complexity, which prevents it from being applied on real-life problem instances. In this paper we propose a novel algorithm for computing anti-alignments, based on the well-known graphbased A ⇤ scheme. By introducing a discount factor in the edit distance used for the search of anti-alignments, we obtain the first efficient algorithm to approximate them. We show how this approximation is quite accurate in practice, by comparing it with the optimal results for small instances where the optimal algorithm can also compute anti-alignments. Finally, we compare the obtained precision metric with respect to the state-of-the-art metrics in the literature for real-life examples.

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“…Since its initial adoption, trace clustering has been proposed as an instrument to reduce variability. Discovering process models from clusters, for example, generally improves quality [14]. An early work in the area, presented by Greco et al [15], uses a set of n-grams to encode a trace activity sequence, thus, transforming traces to feature vectors and input clustering techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Trace clustering techniques have been adopted to solve this issue by identifying sub-logs grouped by trace similarity. This way, by detecting groups with homogeneous behavior, process discovery techniques can be executed in these sub-logs, producing higher quality models, which are instead accessible for stakeholders [14]. Trace clustering has also been studied in the context of explainability for PM [19], and, more recently, adapted to incorporate expert knowledge [18].…”

Section: Introductionmentioning

confidence: 99%

Selecting Optimal Trace Clustering Pipelines with AutoML

Barbon¹,

Ceravolo²,

Damiani³

et al. 2021

Preprint

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Trace clustering has been extensively used to preprocess event logs. By grouping similar behavior, these techniques guide the identification of sub-logs, producing more understandable models and conformance analytics. Nevertheless, little attention has been posed to the relationship between event log properties and clustering quality. In this work, we propose an Automatic Machine Learning (AutoML) framework to recommend the most suitable pipeline for trace clustering given an event log, which encompasses the encoding method, clustering algorithm, and its hyperparameters. Our experiments were conducted using a thousand event logs, four encoding techniques, and three clustering methods. Results indicate that our framework sheds light on the trace clustering problem and can assist users in choosing the best pipeline considering their scenario.

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Prototype Selection Using Clustering and Conformance Metrics for Process Discovery

Cited by 7 publications

References 25 publications

Automated Trace Clustering Pipeline Synthesis in Process Mining

Automated Trace Clustering Pipeline Synthesis in Process Mining

An A^*-Algorithm for Computing Discounted Anti-Alignments in Process Mining

Selecting Optimal Trace Clustering Pipelines with AutoML

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Prototype Selection Using Clustering and Conformance Metrics for Process Discovery

Cited by 7 publications

References 25 publications

Automated Trace Clustering Pipeline Synthesis in Process Mining

Automated Trace Clustering Pipeline Synthesis in Process Mining

An A*-Algorithm for Computing Discounted Anti-Alignments in Process Mining

Selecting Optimal Trace Clustering Pipelines with AutoML

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Product

Resources

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An A^*-Algorithm for Computing Discounted Anti-Alignments in Process Mining