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Digital Transformation at thyssenkrupp: Challenges, Strategies and Examples Read chapter Read first chapter

2017 | OriginalPaper | Chapter

An Approach for Incorporating Expert Knowledge in Trace Clustering

Authors : Pieter De Koninck, Klaas Nelissen, Bart Baesens, Seppe vanden Broucke, Monique Snoeck, Jochen De Weerdt

Published in: Advanced Information Systems Engineering

Publisher: Springer International Publishing

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Abstract

Trace clustering techniques are a set of approaches for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or done by discovering a process model for each cluster of traces. In general, however, it is likely that clustering solutions obtained by these approaches will be hard to understand or difficult to validate given an expert’s domain knowledge. Therefore, we propose a novel semi-supervised trace clustering technique based on expert knowledge. Our approach is validated using a case in tablet reading behaviour, but widely applicable in other contexts. In an experimental evaluation, the technique is shown to provide a beneficial trade-off between performance and understandability.

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previous chapter Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs
next chapter Mining Business Process Stages from Event Logs
Footnotes
1
The algorithm has been implemented as a plugin for ProM 6, and is available on http://​processmining.​be/​expertdriventrac​eclustering/​.
 
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Metadata
Title
An Approach for Incorporating Expert Knowledge in Trace Clustering
Authors
Pieter De Koninck
Klaas Nelissen
Bart Baesens
Seppe vanden Broucke
Monique Snoeck
Jochen De Weerdt
Copyright Year
2017
Book
Advanced Information Systems Engineering

Print ISBN: 978-3-319-59535-1

Electronic ISBN: 978-3-319-59536-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59536-8_35

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