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Expert-driven trace clustering with instance-level constraints

Expert-driven trace clustering with instance-level constraints
Expert-driven trace clustering with instance-level constraints

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.

Constrained clustering, Process mining, Semi-supervised learning, Trace clustering
0219-1377
1197-1220
De Koninck, Pieter
568d7661-550d-4a03-a081-5320da649038
Nelissen, Klaas
7427927d-66f5-49d8-88cb-0970f3364f26
Vanden Broucke, Seppe
89c69367-232e-4c1e-9e57-531bf474e12d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47
De Koninck, Pieter
568d7661-550d-4a03-a081-5320da649038
Nelissen, Klaas
7427927d-66f5-49d8-88cb-0970f3364f26
Vanden Broucke, Seppe
89c69367-232e-4c1e-9e57-531bf474e12d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47

De Koninck, Pieter, Nelissen, Klaas, Vanden Broucke, Seppe, Baesens, Bart, Snoeck, Monique and De Weerdt, Jochen (2021) Expert-driven trace clustering with instance-level constraints. Knowledge and Information Systems, 63 (5), 1197-1220. (doi:10.1007/s10115-021-01548-6).

Record type: Article

Abstract

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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KAIS-D-18-00500_R2 - Accepted Manuscript
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More information

Accepted/In Press date: 9 January 2021
e-pub ahead of print date: 1 March 2021
Published date: May 2021
Additional Information: Funding Information: This research has been financed in part by the EC H2020 MSCA RISE NeEDS Project (Grant agreement ID: 822214) Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Constrained clustering, Process mining, Semi-supervised learning, Trace clustering

Identifiers

Local EPrints ID: 448485
URI: http://eprints.soton.ac.uk/id/eprint/448485
ISSN: 0219-1377
PURE UUID: 9eb5dd38-1910-4583-8cb8-10d37b67ad46
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 23 Apr 2021 16:30
Last modified: 28 Apr 2022 06:15

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Contributors

Author: Pieter De Koninck
Author: Klaas Nelissen
Author: Seppe Vanden Broucke
Author: Bart Baesens ORCID iD
Author: Monique Snoeck
Author: Jochen De Weerdt

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