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• W3201990977 abstract "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." @default.
• W3201990977 created "2021-10-11" @default.
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• W3201990977 date "2021-09-23" @default.
• W3201990977 modified "2023-10-14" @default.
• W3201990977 title "Improving the non‐compensatory trace‐clustering decision process" @default.
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• W3201990977 doi "https://doi.org/10.1111/itor.13062" @default.
• W3201990977 hasPublicationYear "2021" @default.
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