Abstract: In the crowd evacuation process,individuals usually produce the grouping phenomenon according to the intimacy of the relationship.Therefore,the grouping behavior is a factor that can not be neglected in the evacuation simulation of the crowd.The family,friends and colleagues usually form a group according to the degree of intimacy and gather together to a cluster in the evacuation process.The commonly used k-mediods clustering algorithm is sensitive to noise and easy to fall into the local optimum.It can only find the spherical cluster and is sensitive to the selection of the initial clustering center point,which is unsatisfactory in the accuracy of clustering.The DBSCAN algorithm has the advantages of strong ability to deal with noise and can find clusters of arbitrary shape and without specifying the initial clustering center,etc.But it can only identify clusters of similar density.Therefore,this paper proposed a binary DBSCAN clustering algorithm.This algorithm firstly divides the relational data to a grid,then it determines the cluster radius ε according to the density of population of the grid,and finally it executes the DBSCAN clustering algorithm for each grid,so these clusters with different densities can be identified.After clustering,the individual movement is driven in the social force modelwhich adds the individual attraction in the same group.And the influence of the intimacy degree on the aggregation degree is simulated.The experimental results show that,considering the spatial distribution of connected pedestrians in real life,this method has higher clustering accuracy.It can reappear the evacuation situation in the real scene and can be used as an important tool to predict evacuation time and evacuation situation.

Key words: Binary partition, Clustering algorithm, Crowd evacuation simulation, DBSCAN clustering, k-medoids