Optimal location query in road networks is a basic operation in the location intelligence applications.Given a set of clients and servers on a road network,the purpose of optimal location query is to obtain a location...Optimal location query in road networks is a basic operation in the location intelligence applications.Given a set of clients and servers on a road network,the purpose of optimal location query is to obtain a location for a new server,so that a certain objective function calculated based on the locations of clients and servers is optimal.Existing works assume no labels for servers and that a client only visits the nearest server.These assumptions are not realistic and it renders the existing work not useful in many cases.In this paper,we relax these assumptions and consider the k nearest neighbours(KNN)of clients.We introduce the problem of KNN-based optimal location query(KOLQ)which considers the k nearest servers of clients and labeled servers.We also introduce a variant problem called relocation KOLQ(RKOLQ)which aims at relocating an existing server to an optimal location.Two main analysis algorithms are proposed for these problems.Extensive experiments on the real road networks illustrate the efficiency of our proposed solutions.展开更多
基金This paper was supported by the National Nature Science Foundation of China(Grant Nos.61572537,U1501252).
文摘Optimal location query in road networks is a basic operation in the location intelligence applications.Given a set of clients and servers on a road network,the purpose of optimal location query is to obtain a location for a new server,so that a certain objective function calculated based on the locations of clients and servers is optimal.Existing works assume no labels for servers and that a client only visits the nearest server.These assumptions are not realistic and it renders the existing work not useful in many cases.In this paper,we relax these assumptions and consider the k nearest neighbours(KNN)of clients.We introduce the problem of KNN-based optimal location query(KOLQ)which considers the k nearest servers of clients and labeled servers.We also introduce a variant problem called relocation KOLQ(RKOLQ)which aims at relocating an existing server to an optimal location.Two main analysis algorithms are proposed for these problems.Extensive experiments on the real road networks illustrate the efficiency of our proposed solutions.
文摘实际生活中,经常会遇到大规模数据的分类问题,传统k-近邻k-NN(k-Nearest Neighbor)分类方法需要遍历整个训练样本集,因此分类效率较低,无法处理具有大规模训练集的分类任务。针对这个问题,提出一种基于聚类的加速k-NN分类方法 C_kNN(Speeding k-NN Classification Method Based on Clustering)。该方法首先对训练样本进行聚类,得到初始聚类结果,并计算每个类的聚类中心,选择与聚类中心相似度最高的训练样本构成新的训练样本集,然后针对每个测试样本,计算新训练样本集中与其相似度最高的k个样本,并选择该k个近邻样本中最多的类别标签作为该测试样本的预测模式类别。实验结果表明,C_k-NN分类方法在保持较高分类精度的同时大幅度提高模型的分类效率。
