聚類效果往往依賴于密度和相似度的定義，并且當(dāng)數(shù)據(jù)的維增加時(shí)，其復(fù)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(jìn)的共享型最近鄰居聚類算法RSNN，并將RSNN應(yīng)用于高速公路交通數(shù)據(jù)集上，解決了SNN算法在“去噪”、孤立點(diǎn)和代表點(diǎn)的判斷、聚類效果等方面的不足之處。實(shí)驗(yàn)結(jié)果表明，RSNN算法比SNN算法在時(shí)空數(shù)據(jù)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.