A Generic Framework for Trace Clustering in Process Mining

Fareed Zandkarimi · Jana-Rebecca Rehse · Pouya Soudmand · Hartmut Hoehle

45 citations0/0 files storedInternational Conference on Process Mining (ICPM)

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Business Process Modeling and Analysis Data Mining Algorithms and Applications Robotic Process Automation Applications

Abstract

The goal of process discovery is to visualize event log data as a process model. In reality, however, these models are often highly complex. Process trace clustering is a well-studied and powerful technique to address this. It groups an event log into more cohesive sub logs, such that the discovered process models become less complex and easier to understand. Over the past 15 years, researchers proposed various approaches for trace clustering in process discovery. The developed approaches vary greatly with regard to algorithmic capacities, data characteristics, computational complexity, and integration of additional information. In this paper, we provide a state-of-the-art analysis of trace clustering by a) performing a systematic literature review, and b) proposing a generic framework for trace clustering. Eventually, our goal is to provide an overview of current trace clustering research and a basis for developing new methods and approaches to trace clustering.

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References

2002Analyzing the Past to Prepare for the Future: Writing a Literature Review 2016Process Mining 2009Trace Clustering in Process Mining 2006Discovering expressive process models by clustering log traces 2009Context Aware Trace Clustering: Towards Improving Process Mining Results 2016Clustering-Based Predictive Process Monitoring 2013Active Trace Clustering for Improved Process Discovery 2010Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models

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