ABSTRACT

Spatial trajectory data, interestingly attracting organizations to obtain mobility-based activity patterns of smartphone users. One of the basic objective in this regard is the determination of ‘stops’ or technically high-density points in the trajectory data. Most works carried out in this area uses variants of density-based clustering algorithms for determining stop points. One of the notable challenges in this area is the determination of the parameters for the clustering algorithm, which highly affects the accuracy of detecting the ‘stops’.In this paper a semi-automatic approach is proposed based on particle swarm optimization, DBSCAN, and S_Dbw internal validity index for determining appropriate parameter values for the clustering algorithm and fast convergence.

摘要

空间轨迹数据，有趣地吸引组织获取智能手机用户基于移动性的活动模式。这方面的基本目标之一是确定轨迹数据中的“停止”或技术上的高密度点。在该领域进行的大多数工作都使用基于密度的聚类算法的变体来确定停止点。该领域的一个显着挑战是确定聚类算法的参数，这极大地影响了检测“停止”的准确性。本文提出了一种基于粒子群优化、DBSCAN 和S_Dbw 内部有效性指标，用于为聚类算法和快速收敛确定合适的参数值。