Ainhoa Yera Gil, Iñigo Perona Balda, Olatz Arbelaitz Gallego, Javier Muguerza Rivero
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.
Lan honetan UPV/EHUko matrikulazioaren inguruko eZerbitzuko nabigazioa aztertu dugu webmeatzaritzako tekniken bidez, nabigazio-saioen sailkapena era automatikoan egiten saiatu gara.
Emaitzen arabera, baieztatu dugu maila handi batean definitutako arrakasta / porrota nabigazioportaerak detektatzeko gai garela. Esaterako, arrakasta gisa etiketatu diren klusterretan dauden saioen % 90etik gora arrakasta motakoak dira eta porroten kasuan % 90 inguru. Gainera, gainbegiratutako ikasketa bidez, bi saio-motak era automatikoan desberdintzeko gai gara % 96ko asmatze-tasa batekin.
Beraz, lan hau etorkizunean eZerbitzu hau hobetu ahal izateko oinarri ona dela uste dugu.
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