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2017 | OriginalPaper | Buchkapitel

An Approach for Incorporating Expert Knowledge in Trace Clustering

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

Erschienen in: Advanced Information Systems Engineering

Verlag: 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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Fußnoten
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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Metadaten
Titel
An Approach for Incorporating Expert Knowledge in Trace Clustering
verfasst von
Pieter De Koninck
Klaas Nelissen
Bart Baesens
Seppe vanden Broucke
Monique Snoeck
Jochen De Weerdt
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-59536-8_35