%0 Journal Article
%T Clustering Objects in a Road Network<br>基于道路网络的对象聚类
%A CHEN Ji-Dong
%A MENG Xiao-Feng
%A LAI Cai-Feng
%A <br>陈继东
%A 孟小峰
%A 赖彩凤
%J 软件学报
%D 2007
%I 
%X Most spatial clustering algorithms deal with the objects in Euclidean space. In many real applications, however, the accessibility of spatial objects is constrained by spatial networks (e.g. road network). It is therefore more realistic to work on clustering objects in a road network. The distance metric in such setting is redefined by the network distance, which has to be computed by the expensive shortest path distance over the network. The existing methods are not applicable to such cases. Therefore, by exploiting unique features of road networks, two new clustering algorithms are presented, which use the information of nodes and edges in the network to prune the search space and avoid some unnecessary distance computations. The experimental results indicate that the algorithms achieve high efficiency for clustering objects in real road network.
%K data mining
%K clustering
%K road network
%K spatial object
%K network distance
%K shortest path<br>数据挖掘
%K 聚类
%K 道路网络
%K 空间对象
%K 网络距离
%K 最短路径
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=DE357C1A637B3BDA&yid=A732AF04DDA03BB3&vid=13553B2D12F347E8&iid=0B39A22176CE99FB&sid=2E01F39B6CBD53DE&eid=A04F01817ECB9A48&journal_id=1000-9825&journal_name=软件学报&referenced_num=0&reference_num=23