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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/ADA7D9A5-04FB-444E-8833-FAFA15192C58 | - |
| Identifier | https://doi.org/10.1111/itor.13062 | - |
| Identifier | https://onlinelibrary.wiley.com/doi/10.1111/itor.13062 | - |
| Language | en | - |
| Extent | 20 pages | en |
| Title | Improving the non-compensatory trace-clustering decision process | en |
| Creator | Delias Pavlos | en |
| Creator | Δελιας Παυλος | el |
| Creator | Doumpos Michail | en |
| Creator | Δουμπος Μιχαηλ | el |
| Creator | Grigoroudis Evangelos | en |
| Creator | Γρηγορουδης Ευαγγελος | el |
| Creator | Matsatsinis Nikolaos | en |
| Creator | Ματσατσινης Νικολαος | el |
| Publisher | Wiley | en |
| Content Summary | In 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 Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2022-10-03 | - |
| Date of Publication | 2021 | - |
| Subject | Process mining | en |
| Subject | Trace clustering | en |
| Subject | Outranking methods | en |
| Subject | Robustness | en |
| Bibliographic Citation | P. 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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