20.500.12556/RUL-125334 Time series clustering of online gambling activities for addicted users' detection Ever since the worldwide demand for gambling services started to spread, its expansion has continued steadily. To wit, online gambling is a major industry in every European country, generating billions of Euros in revenue for commercial actors and governments alike. Despite such evidently beneficial effects, online gambling is ultimately a vast social experiment with potentially disastrous social and personal consequences that could result in an overall deterioration of social and familial relationships. Despite the relevance of this problem in society, there is a lack of tools for characterizing the behavior of online gamblers based on the data that are collected daily by betting platforms. This paper uses a time series clustering algorithm that can help decision-makers in identifying behaviors associated with potential pathological gamblers. In particular, experimental results obtained by analyzing sports event bets and black jack data demonstrate the suitability of the proposed method in detecting critical (i.e., pathological) players. This algorithm is the first component of a system developed in collaboration with the Portuguese authority for the control of betting activities. human behavior modeling online gambling machine learning true false true Angleški jezik Ni določen Članek v reviji 2021-03-11 09:31:30 2021-03-11 09:40:51 2022-09-05 03:50:13 0000-00-00 00:00:00 2021 0 0 31 str. iss. 5, art. 2397 Vol. 11 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004:78 2076-3417 10.3390/app11052397 54522627 522979353 RAZ_Peres_Fernando_2021.pdf RAZ_Peres_Fernando_2021.pdf 1 D3E9CEDDAF45225A49282344376C2C02 92ecce990b4e6b4fdf3c61a47e6d974dca3a480a41fcd1a58bd84876a04f9f83 615cf622-9ae1-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=154217 https://www.mdpi.com/2076-3417/11/5/2397 0 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=141017 Ekonomska fakulteta 0 0 0