Reinforcement Learning for the Detection of Arbitrary Shaped Spatial Clusters

Investigations of spatial clustering patterns arouse interest in several areas. The most widely used technique for this objective is the Spatial Scan Statistic. However, the Scan statistics alone are ineffective when clusters may have an irregular shape. Other methodologies are used to increase flexibility for possible cluster shapes. This article proposed an approach through Reinforcement Learning with Scan spatial statistics as the reward function. The new methodology is presented in detail and tested in a vast controlled experimental set to ensure its adaptability to the problem of detecting spatial clusters. The results show that the new technique is promising for the problem under study.