A spatial clustering algorithm for line objects based on extended Hausdorff distance

Spatial clustering is one of the most commonly used approaches to spatial data mining. This study proposes an algorithm for clustering spatial line objects. Proximity between lines is measured using the extended Hausdorff distance. Lines are clustered using an improved K-means procedure. The procedure defines the kernel line of a cluster and the kernel lines of all clusters are updated in each iteration The algorithm is applied to tropical cyclone tracks of 20 years in the western Northern Pacific. Results show that the algorithm can partition lines to clusters that agree with human cognition.