聚類效果往往依賴于密度和相似度的定義，并且當數據的維增加時，其復雜度也隨之增加。該文基于共享型最近鄰居聚類算法SNN，提出了一種改進的共享型最近鄰居聚類算法RSNN，并將RSNN應用于高速公路交通數據集上，解決了SNN算法在“去噪”、孤立點和代表點的判斷、聚類效果等方面的不足之處。實驗結果表明，RSNN算法比SNN算法在時空數據集上具有更好的聚類效果。

Clustering results often depend on density and similarity critically, and its complexity often changes along with the augment of sample dimensionality. This paper refers to classical shared nearest neighbor clustering algorithm (SNN) and refined shared nearest neighbor clustering algorithm (RSNN). By applying this RSNN algorithm on freeway traffic data set, we settled several problems existed in SNN algorithm, such as outliers, statistic, core points, computation complexity and so on. Experiment results prove that this refined algorithm has better clustering results on multi-dimensional data set than SNN algorithm.