基于道路网络的约束聚类

聚类法是最重要的空间数据分析方法之一。在大多数真实的应用中，对受限于网络空间的数据访问和分析更有意义（如道路网络上对象的分析）。目前已有一些道路网络聚类方法，但都是基于理想的抽象空间，没有考虑现实存在的约束条件（如交通限制信息），因此不能直接应用到现实环境中。本文提出了带约束条件的道路网络模型，扩展了已有的聚类算法。算法的实验结果表明，算法对真实的道路网络中的对象聚类是高效的。

Constraint Clustering Based on a Road Network

Clustering is one of the most important analysis tasks in spatial databases.However,in many real applications,it is more meaningful to work on clustering objects in a constrained spatial network(e.g.road network).The existing methods do not consider the constrained condition(e.g.traffic information),which is based on an ideal and Abstract network.It is therefore difficult to apply them to a real road network.This paper proposes a model of clustering objects in a road network with constrained conditions and improves the existing algorithms The experimental results show that the model achieves a high efficiency.