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

Seeliger, Alexander and Nolle, Timo and Mühlhäuser, Max
Weske, Mathias and Montali, Marco and Weber, Ingo and vom Brocke, Jan (eds.) (2018):
Finding Structure in the Unstructured: Hybrid Feature Set Clustering for Process Discovery.
In: Business Process Management, Cham, Springer International Publishing, Sydney, Australia, In: 16, DOI: 10.1007/978-3-319-98648-7_17,
[Conference or Workshop Item]

Abstract

Process discovery is widely used in business process intelligence to reconstruct process models from event logs recorded by information systems. With the increase of complexity and flexibility of processes, it is getting more and more challenging for discovery algorithms to generate accurate and comprehensive models. Trace clustering aims to overcome this issue by splitting event logs into smaller behavioral similar sub-logs. From these sub-logs more accurate and comprehensive process models can be reconstructed. In this paper, we propose a novel clustering approach that uses frequent itemset mining on the case attributes to also reveal relationships on the data perspective. Our approach includes this additional knowledge as well as optimizes the fitness of the underlying process models of each cluster to generate accurate clustering results. We compare our method with six other clustering methods and evaluate our approach using synthetic and real-life event logs.

Item Type: Conference or Workshop Item
Erschienen: 2018
Editors: Weske, Mathias and Montali, Marco and Weber, Ingo and vom Brocke, Jan
Creators: Seeliger, Alexander and Nolle, Timo and Mühlhäuser, Max
Title: Finding Structure in the Unstructured: Hybrid Feature Set Clustering for Process Discovery
Language: English
Abstract:

Process discovery is widely used in business process intelligence to reconstruct process models from event logs recorded by information systems. With the increase of complexity and flexibility of processes, it is getting more and more challenging for discovery algorithms to generate accurate and comprehensive models. Trace clustering aims to overcome this issue by splitting event logs into smaller behavioral similar sub-logs. From these sub-logs more accurate and comprehensive process models can be reconstructed. In this paper, we propose a novel clustering approach that uses frequent itemset mining on the case attributes to also reveal relationships on the data perspective. Our approach includes this additional knowledge as well as optimizes the fitness of the underlying process models of each cluster to generate accurate clustering results. We compare our method with six other clustering methods and evaluate our approach using synthetic and real-life event logs.

Journal or Publication Title: Business Process Management. BPM 2018. Lecture Notes in Computer Science
Title of Book: Business Process Management
Series Name: 16
Place of Publication: Cham
Publisher: Springer International Publishing
Edition: 11080
Uncontrolled Keywords: Knowledge discovery, Process discovery, Trace clustering, Process Mining, Business Process Intelligence
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Event Location: Sydney, Australia
Date Deposited: 19 Jun 2018 09:47
DOI: 10.1007/978-3-319-98648-7_17
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