Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/E8A54329-CA15-466B-8D4A-F5C3907A8C2E | - |
| Αναγνωριστικό | https://onlinelibrary.wiley.com/doi/abs/10.1111/itor.12395 | - |
| Αναγνωριστικό | https://doi.org/10.1111/itor.12395 | - |
| Γλώσσα | en | - |
| Τίτλος | A non-compensatory approach for trace clustering | en |
| Δημιουργός | Delias Pavlos | en |
| Δημιουργός | Δελιας Παυλος | el |
| Δημιουργός | Doumpos Michael | en |
| Δημιουργός | Δουμπος Μιχαηλ | el |
| Δημιουργός | Grigoroudis Evangelos | en |
| Δημιουργός | Γρηγορουδης Ευαγγελος | el |
| Δημιουργός | Matsatsinis Nikolaos | en |
| Δημιουργός | Ματσατσινης Νικολαος | el |
| Εκδότης | The International Federation of Operational Research Societies | en |
| Περίληψη | One of the main functions of process mining is the automated discovery of process models from event log files. However, in flexible environments, such as healthcare or customer service, delivering comprehensible process models can be very challenging, mainly due to the complexity of the registered logs. A prevalent response to this problem is trace clustering, that is, grouping behaviors and discovering a distinct model per group. In this paper, we propose a novel trace clustering technique inspired from the outranking relations theory. The proposed technique can handle multiple criteria with strongly heterogeneous scales, and it allows a non-compensatory logic to guide the creation of a similarity metric. To reach this, we use three key components: We separate factors that are in favor of the similarity from those that are not, through discrimination thresholds; we provide non-concordant factors with a "veto" power; and we aggregate all factors into an overall metric. We evaluated this novel, non-compensatory approach against two of the most spotlighted trace clustering functions: variants' identification and model complexity reduction. Results suggest that the proposed technique can be used at both functions with compelling performance. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-06-21 | - |
| Ημερομηνία Δημοσίευσης | 2017 | - |
| Θεματική Κατηγορία | Multiple criteria decision aid | en |
| Θεματική Κατηγορία | Process mining | en |
| Θεματική Κατηγορία | Trace clustering | en |
| Βιβλιογραφική Αναφορά | P. Delias, M. Doumpos, E. Grigoroudis and N. Matsatsinis, "A non-compensatory approach for trace clustering," Int. T. Oper. Res., Feb. 2017. doi: 10.1111/itor.12395 | en |
Copyright © DIAS 2013
