UPV/EHUko eZerbitzu baten modelatzea ikasketa automatikoaren bidez
- Iñaki Alegria (ed. lit.)
- Ainhoa Latatu (ed. lit.)
- Miren Josu Omaetxebarria (ed. lit.)
- Patxi Salaberri (ed. lit.)
Verlag: Udako Euskal Unibertsitatea, UEU = Universidad Vasca de Verano
ISBN: 978-84-8438-627-8, 978-84-8438-632-2
Datum der Publikation: 2017
Titel des Bandes: Ingenieritza eta Arkitektura
Band: 5
Ausgabe: 5
Seiten: 111-118
Kongress: Ikergazte. Nazioarteko Ikerketa Euskaraz (2. 2017. Iruñea)
Art: Konferenz-Beitrag
Zusammenfassung
In this work we have analyzed the enrollment eService navigation of the UPV/EHU and using data mining techniques we have attempted to automatically perform navigation sessions classification. The results show that we are able to detect the defined success and failure navigation behaviours. For example, more than 90 % of the sessions of the clusters labelled as success are of success type and in the failure case, around 90%. Besides, using supervised learning we are able to automatically distinguish the two nabigation types with an accuracy rate of 96 %. Thus, we think that this research is a suitable basis to improve the eService analyzed in a near future.