Abstract:

A clustering algorithm named quick density based spatial clustering of applications with noise (QDBSCAN) is proposed for the analysis and application of wargaming data. By detecting the isolated points, the QDBSCAN is used to determine the vulnerability of ground units’ deployment rapidly. Compared with density based spatial clustering of applications with noise (DBSCAN), the QDBSCAN makes some improvements in such aspects: Define the shortest viable path as the similarity measurement to make the clustering algorithm more coincident with the rules of computer wargames, set the density parameters dynamically instead of statically, choose a small number of representative objects to expand the cluster, which reduces the execution frequency of region query; groups the whole dataset by divisiory regions to reduce the scale of clustering. Experimental results indicate that the QDBSCAN is more effective and efficient than the DBSCAN in clustering large datasets.