摘要：We propose StarScan, a new star-shaped scan statistic for detecting irregularly-shaped spatial clusters. StarScan generalizes the traditional, circular spatial scan statistic by allowing the radius of the cluster around a center location to vary continuously with the angle, but penalizes the log-likelihood ratio score proportional to the total change in radius. StarScan was compared with circular scan and fast subset scan on simulated respiratory outbreaks and bioterrorist anthrax attacks injected into real-world Emergency Department data. Given a small amount of labeled training data, StarScan learns appropriate penalties for both compact and elongated clusters, resulting in improved detection performance.
