Spatial scan statistics: approximations and performance study

Spatial scan statistics are used to determine hotspots in spatial data, and are widely used in epidemiology and biosurveillance. In recent years, there has been much effort invested in designing efficient algorithms for finding such "high discrepancy" regions, with methods ranging from fast heuristics for special cases, to general grid-based methods, and to efficient approximation algorithms with provable guarantees on performance and quality. In this paper, we make a number of contributions to the computational study of spatial scan statistics. First, we describe a simple exact algorithm for finding the largest discrepancy region in a domain. Second, we propose a new approximation algorithm for a large class of discrepancy functions (including the Kulldorff scan statistic) that improves the approximation versus runtime trade-off of prior methods. Third, we extend our simple exact and our approximation algorithms to data sets which lie naturally on a grid or are accumulated ...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Deepak Agarwal, Andrew McGregor, Jeff M. Phillips, Suresh Venkatasubramanian, Zhengyuan Zhu
• Keywords: Data Mining, Discrepancy Maximization Problem, Efficient Approximation Algorithms, KDD 2006, Spatial Scan Statistics