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聚类分析算法在交通控制中的应用 被引量:11

An Application of Cluster Analysis Algorithm in Traffic Control
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摘要 聚类分析是根据物理或抽象对象间的相似程度对对象进行分类的一种方法,通过聚类分析使得同一类中的对象具有高的相似度,而与其他类中的对象则很不相同。PAM(PartitioningAroundMedoids)算法是一种基于距离的分离式聚类方法,具有良好的抗噪声、抗偏离点的能力。本文将PAM算法应用于交通控制的时段划分中,通过验证分析,结果表明取得了良好的分类效果。 Cluster analysis is a process of grouping a set of physical or abstract objects into classes of similar objects. And the objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Partitioning Around Medoids(PAM) is a partitioning algorithm based on distance measure, and it still works well in the presence of noise and outlier. In this paper, we apply the PAM algorithm into the time-partition of the traffic control. The validation analysis proves that a good clustering result is achieved.
出处 《系统工程》 CSCD 北大核心 2004年第2期66-68,共3页 Systems Engineering
关键词 交通控制 聚类分析算法 数据挖掘 交通流量 PAM算法 Data Mining Cluster Analysis Traffic Control
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参考文献4

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