Anomaly Region Detection Based on DMST

Sulan Zhang and Jiaqiang Wan
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Sulan Zhang: College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing, China
Jiaqiang Wan: Country Garden, Foshan City, Guangdong, China

International Journal of Data Warehousing and Mining (IJDWM), 2019, vol. 15, issue 1, 39-57

Abstract: Anomaly region detection aims at finding spatial outliers or spatial anomalous clusters. Generally, detection approaches cover spatial neighbor's discovery with spatial attributes and anomaly measurement of spatial regions according to non-spatial attributes. In this article, an anomaly region detection method using Delaunay minimal spanning tree (DMST for short) is proposed. First, a Delaunay minimal spanning tree is constructed. Then, the current longest edge of the tree is iteratively cut and anomaly regions are concurrently detected. Finally, the shortest edge of the related bipartite graph is taken as the anomaly measurement. The proposed method could avoid the disturbance of bad reference neighbors and generate anomaly regions keeping atomicity.

Date: 2019
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