# Time Series Clustering of Online Gambling Activities for Addicted Users' Detection
## 5 Conclusions  
In 2012, the European Commission released a statement highlighting the need for regulatory policies to aid in the detection of pathological gambling behaviors, citing \"a responsibility to protect those citizens and families who suffer from a gambling addiction.\"  
To answer this call, in this paper, we propose an ML-based system to identify different profiles of users, aiming at discovering the high-risk pathological gamblers. To tackle this problem, we rely on a K-means algorithm based on the dynamic time warping algorithm, a well-known technique that makes it possible to align and compare time series. Data provided by the Portuguese authority for the control of gambling activities were considered. In particular, the data represent one month of online gambling activities collected by ten gambling providers operating in Portugal. The data contain different kinds of gambling modalities, including sports and black jack bets' data used in this paper.  
After a preprocessing phase aimed at building the time series for each player and the subsequent choice of the algorithm's parameter (i.e., the number of clusters to be formed), the clustering algorithm under the DTW was executed. The clusters created by the algorithm allowed for an analysis of the different users' profiles. In particular, for both the considered gambling modalities (i.e., black jack and sports bets), four clusters were obtained. From the analysis of these clusters, it was possible to characterize the respective users' profiles. In particular, we were able to \"label\" the four clusters as follows: professional players, occasional players, regular players, pathological gamblers. Interestingly, the number of players in the \"pathological gamblers\" matched the existing knowledge of the gambling authority concerning the spread of this phenomenon. Moreover, a subsequent analysis allowed identifying in this cluster some users that asked for a self-exclusion from betting activities (thus recognizing themselves as potentially pathological gamblers).  
The results obtained suggest the suitability of the considered algorithm in identifying potentially critical players. In particular, the identification of critical clusters with a limited number of players may simplify the subsequent analysis of the gambling control authority, which can focus its efforts and resources on the analysis of a small fraction of users.  
As future work, we plan to build a predictive model for each of the identified cluster. This would allow predicting the behavior of the users and detecting anomalous behaviors.  
Figure 24: Time series plot of two potential medium-risk pathological gamblers.  
This information may indicate that a specific user is at risk of developing pathological gambling, and it can be used by the gambling control authority for taking specific actions.  
[PERSON] designed and implemented the system; [PERSON] performed the data preprocessing; [PERSON], and [PERSON] provided the data; [PERSON] supervised the work; [PERSON] and [PERSON] provided funding; [PERSON] and [PERSON] executed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by national funds through FCT (Fundacao para a Ciencia e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET). [PERSON] acknowledges the financial support from the Slovenian Research Agency (research core funding No. P5-0410). We acknowledge the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support.  
Not applicable.  
Data Availability StatementData were provided by the Gambling Inspection and Regulation Service of Portugal. Due to the existing regulation, data cannot be made available.  
The authors declare no conflict of interest.