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Improving the non-compensatory trace-clustering decision process

Delias Pavlos, Doumpos Michail, Grigoroudis Evangelos, Matsatsinis Nikolaos

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URIhttp://purl.tuc.gr/dl/dias/ADA7D9A5-04FB-444E-8833-FAFA15192C58-
Identifierhttps://doi.org/10.1111/itor.13062-
Identifierhttps://onlinelibrary.wiley.com/doi/10.1111/itor.13062-
Languageen-
Extent20 pagesen
TitleImproving the non-compensatory trace-clustering decision processen
CreatorDelias Pavlosen
CreatorΔελιας Παυλοςel
CreatorDoumpos Michailen
CreatorΔουμπος Μιχαηλel
CreatorGrigoroudis Evangelosen
CreatorΓρηγορουδης Ευαγγελοςel
CreatorMatsatsinis Nikolaosen
CreatorΜατσατσινης Νικολαοςel
PublisherWileyen
Content SummaryIn flexible environments (such as healthcare or customer service), the observed behavior is expected to considerably vary, namely there is no dominant flow path. Such a high variability obstructs the process discovery task since it regularly leads to “spaghetti” process models. Trace clustering is about grouping behaviors, and discovering a distinct model per group, thus delivering more comprehensible results. In previous works, we have proposed a multiple-criteria non-compensatory approach to create a similarity metric and finally perform trace clustering. The main problem that we tried to respond to is how to summarize a process event log, when a lot of variability exists, thus facilitating knowledge discovery. The underpinnings of the non-compensatory approach are first the fact that a sufficient number of criteria must be concordant with the similarity (concordance setting) and second that there should not exist any criterion raising a veto logic, that is, among the criteria that are not concordant, none of them must be conflicting with the similarity (discordance setting). This work challenges improved support for the decision-maker (DM) and it extends the previous approach by (i) proposing an improved clustering technique based on spectral clustering; (ii) guiding the clustering process by allowing reinforced or counterveto effects and pairwise constraints; (iii) handling outliers through a trimming approach as an integer linear program. All improvements aiming at making elements of the trace-clustering process more accessible to the DMs and enhancing the understandability of the analysis.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2022-10-03-
Date of Publication2021-
SubjectProcess miningen
SubjectTrace clusteringen
SubjectOutranking methodsen
SubjectRobustnessen
Bibliographic CitationP. Delias, M. Doumpos, E. Grigoroudis, and N. Matsatsinis, “Improving the non‐compensatory trace‐clustering decision process,” Intl. Trans. in Op. Res., early access, doi: 10.1111/itor.13062.en

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