CAJ | 학술논문

针对现有聚类分析算法对平面空间时空轨迹进行聚类的不足，提出了基于区域的快速聚类方法。借助先验知识确定输入参数，使用符合现实情况的切比雪夫距离或曼哈顿距离度量点间距离以划分聚类簇，区分人的各个常驻地点；使用区域叠加的概念度量点的密度，确定簇的质心以获得每个常驻地点的中心坐标；在保证算法复杂度的前提下确定簇的边缘点，标注人在每个常驻地点的活动范围。相比现有算法，该算法更适用于时空轨迹数据分析。
침대현유취류분석산법대평면공간시공궤적진행취류적불족，제출료기우구역적쾌속취류방법。차조선험지식학정수입삼수，사용부합현실정황적절비설부거리혹만합돈거리도량점간거리이화분취류족，구분인적각개상주지점；사용구역첩가적개념도량점적밀도，학정족적질심이획득매개상주지점적중심좌표；재보증산법복잡도적전제하학정족적변연점，표주인재매개상주지점적활동범위。상비현유산법，해산법경괄용우시공궤적수거분석。
In contrast to the deficiency in the existing clustering algorithms for spatio-temprol trajectory in planar space, put forward the fast clustering algorithm based on region, with the aid of prior knowledge to determine the input parameters, utilize Chebyshev distance or Manhattan distance which conform to the reality to measure the dis-tance between two points to partition cluster, therefore distinguish the resident place. Utilize the concept of region to measure the density of points, therefore determine the cluster center in order to get the center coordinate in each resident place. On the premise of guaranteeing the algorithm complexity to determine the bunch of edge points and denote people’ s activities scope in each resident place. Compared with the existing algorithms, the proposed algo-rithm is more suitable for spatio-temprol trajectory analysis.