Density Clustering with Adaptive Neighbors and Its Application to Accident Black Spots Recognition
Clustering is one of the main methods for recognizing large-scale traffic accident black spots.However,its main problem is that the number of traffic accident-prone areas cannot be determined in advance,which means the number of clusters cannot be known.The paper adopts adjacent probability to define the density of data points.Based on it,the clustering centers are determined.Then the data points are clustering into groups according to their relationship with the cluster centers.The results show that:firstly,the algorithm is insensitive to the parameters,which means it is practicable in general;secondly,the algorithm can automatically determine the number of clusters;thirdly,the algorithm's clustering process only relies on local density and neighbor points,which can identify noisy points and improve the accuracy of the results.The proposed algorithm are tested on some real datasets,and the clustering results are compared with the results obtained by other algorithms using evaluation indexes ARI(Adjusted Rand Index)and NMI(Normalized Mutual Information).The algorithm is then used to cluster traffic accidents on the data of six US states,the experimental results indi-cate that the algorithm has a good adaptability to traffic accidents and can find the traffic accident-prone areas well.
traffic accidents black spotsclustering algorithmsnumber of clustersclustering with adaptive neighborslocal density
万方数据
维普
