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Finding Structure in the Unstructured: Hybrid Feature Set Clustering for Process Discovery · Study

WorkFinding Structure in the Unstructured: Hybrid Feature Set Clustering for Process Discovery

Alexander Seeliger2018

Work2018

Finding Structure in the Unstructured: Hybrid Feature Set Clustering for Process Discovery

Alexander Seeliger · Timo Nolle · Max Mühlhäuser

13 citations·0/0 files stored·Lecture notes in computer science

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

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References

2011Process Mining 2011Flexible Heuristics Miner (FHM)2009Trace Clustering in Process Mining 2006Discovering expressive process models by clustering log traces 2009Context Aware Trace Clustering: Towards Improving Process Mining Results 2012A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs 2013Active Trace Clustering for Improved Process Discovery 2010Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models

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2019Scalable Mixed-Paradigm Trace Clustering using Super-Instancesother · 10 shared references 2016Explaining clusterings of process instancesarticle · 9 shared references 2015The Use of Process Mining in Business Process Simulation Model Constructionarticle · 7 shared references 2016Clustering Traces Using Sequence Alignmentother · 7 shared references 2021Expert-driven trace clustering with instance-level constraintsarticle · 7 shared references 2020A Generic Framework for Attribute-Driven Hierarchical Trace Clusteringother · 7 shared references

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Year2018

Typeother

Published2018-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations13

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

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doi10.1007/978-3-319-98648-7_17

openalex_workW2885022903

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