Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/9A3240E4-1119-4F68-B125-FDAF038BDAA0 | - |
| Αναγνωριστικό | http://www.sciencedirect.com/science/article/pii/S0950705115001501 | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.knosys.2015.04.012 | - |
| Γλώσσα | en | - |
| Μέγεθος | 11 pages | en |
| Τίτλος | Supporting healthcare management decisions via robust clustering of event logs | en |
| Δημιουργός | Delias Pavlos | en |
| Δημιουργός | Δελιας Παυλος | el |
| Δημιουργός | Michael Doumpos | en |
| Δημιουργός | Δουμπος Μιχαλης | el |
| Δημιουργός | Grigoroudis Evangelos | en |
| Δημιουργός | Γρηγορουδης Ευαγγελος | el |
| Δημιουργός | Manolitzas Panagiotis | en |
| Δημιουργός | Μανωλιτζας Παναγιωτης | el |
| Δημιουργός | Matsatsinis Nikolaos | en |
| Δημιουργός | Ματσατσινης Νικολαος | el |
| Εκδότης | Elsevier | en |
| Περίληψη | Business processes constitute an essential asset of organizations while the related process models help to better comprehend the process and therefore to enable effective process analysis or redesign. However, there are several working environments where flows are particularly flexible (e.g., healthcare, customer service) and process models are either very hard to get created, or they fail to reflect reality. The aim of this paper is to support decision-making by providing comprehensible process models in the case of such flexible environments. Following a process mining approach, we propose a methodology to cluster customers’ flows and produce effective summarizations. We propose a novel method to create a similarity metric that is efficient in downgrading the effect of noise and outliers. We use a spectral technique that emphasizes the robustness of the estimated groups, therefore it provides process analysts with clearer process maps. The proposed method is applied to a real case of a healthcare institution delivering valuable insights and showing compelling performance in terms of process models’ complexity and density. | 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 | 2015-11-08 | - |
| Ημερομηνία Δημοσίευσης | 2015 | - |
| Θεματική Κατηγορία | Process mining | en |
| Θεματική Κατηγορία | Clustering | en |
| Θεματική Κατηγορία | Robustness | en |
| Θεματική Κατηγορία | Knowledge discovery | en |
| Θεματική Κατηγορία | Healthcare management | en |
| Βιβλιογραφική Αναφορά | P. Delias, M. Doumpos, E. Grigoroudis, P. Manolitzas and N. Matsatsinis, "Supporting healthcare management decisions via robust clustering of event logs," Knowl.-Based Syst., vol. 84, pp. 203-213, Aug. 2015. doi:10.1016/j.knosys.2015.04.012 | en |
Copyright © DIAS 2013
