关键词: 道路网络, 移动对象, 聚类, 时空相似系数, 数据挖掘

Abstract: Existing objects based on road network clustering algorithm eb-cls uses the network distance to describe similarity between moving objects. It does not make full use of time and space properties of the objects, which may lead to the algorithm can’t reflect the movement pattern of mobile object dynamic evolution, frequently updated clustering results, not satisfactory clustering accuracy, low efficiency and so on. Aimed at these deficiencies, this paper puts forward the Moving Objects Based on Road Network （MOBORN） clustering algorithm. It introduces the similarity coefficient of time and space, takes the speed, the direction and the position of mobile object into account. When the similarity coefficient of space and time between moving objects reach to the given threshold, it assigns them to the same cluster, and at the same time, dynamically maintenances clustering results, reduces the time of clustering. The experimental results show that the algorithm achieves 97% accuracy and 40% higher efficiency for clustering moving objects in real road network than the eb-cls algorithm.

Key words: road network, moving objects, clustering, temporal and spatial similarity coefficient, data mining