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 is a major industry in every European country, generating billions of Euros revenue commercial actors and governments alike. Despite such evidently beneficial effects, ultimately vast social experiment with potentially disastrous personal consequences that could result an overall deterioration familial relationships. relevance this problem society, there lack tools characterizing behavior gamblers based on data are collected daily by betting platforms. This paper uses time series clustering algorithm can help decision-makers identifying behaviors associated potential pathological gamblers. In particular, experimental results obtained analyzing sports event bets black jack demonstrate suitability proposed method detecting critical (i.e., pathological) players. first component system developed collaboration Portuguese authority control activities.