Journal
KNOWLEDGE AND INFORMATION SYSTEMS
Volume 63, Issue 5, Pages 1197-1220Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s10115-021-01548-6
Keywords
Trace clustering; Process mining; Semi-supervised learning; Constrained clustering
Funding
- EC H2020 MSCA RISE NeEDS Project [822214]
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This paper introduces two constrained trace clustering techniques that can leverage expert knowledge in the form of instance-level constraints. Experimental results show that these new techniques are capable of generating clustering solutions that are more justifiable without significantly impacting their quality.
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, however, is that their solutions are usually hard to evaluate or justify by domain experts. In this paper, we present two constrained trace clustering techniques that are capable to leverage expert knowledge in the form of instance-level constraints. In an extensive experimental evaluation using two real-life datasets, we show that our novel techniques are indeed capable of producing clustering solutions that are more justifiable without a substantial negative impact on their quality.
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