Abstract:
Studies on spatial cluster patterns are of interest in many areas. Spatial scan statistics is the most widespread strategy for studying these patterns. However, scan statistics lose substantial efficiency in situations where candidate clusters can assume irregular shapes. Conversely, other techniques, with the aim of increasing the flexibility of analyzing cluster shapes, have emerged. We present two novel reinforcement learning approaches that use scan spatial statistics to represent the reward function. The novel approaches are explained in detail, and there is an extensive set of computational experiments with controlled synthetic data to verify their functionality and adaptation to the problem of detecting spatial clusters. Our results attest to the quality and applicability of the new techniques for addressing this problem.
