ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization

The spatial scan statistic (SaTScan) has become one of the most popular methods for detecting and evaluating spatial clusters. However, this method can only identify circular or elliptical clusters and is not a good fit for the detection of irregularly shaped clusters. Numerous methods have since been proposed to solve this problem. Nevertheless, if multiple clusters coexist, these methods may not identify the correct situation, because the interference between clusters can easily lead to a tree-like shaped cluster and cause confusion in the results. In this paper, we propose an Ant Colony Optimization based Multiple Cluster Detection (ACOMCD) algorithm, which combines classical SaTScan with the ant colony optimization (ACO) approach. In the initial stage, SaTScan is first used to mark the candidate cluster areas according to the significance of their maximum likelihood evaluations. Then ACO-based scan statistic is carried out separately on these candidate clusters to identify their natural shapes. The algorithm was designed for spatial regional count data only. Comparisons between ACOMCD, SaTScan, GaScan (genetic algorithm-based scan statistic), and FleXScan (flexibly shaped spatial scan statistic) on three kinds of simulated datasets show that ACOMCD performs the best in simultaneously determining the exact number of clusters and identifying multiple irregularly shaped clusters. A case study on esophageal cancer in eastern China further validates the correctness and effectiveness of ACOMCD.