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OPTICS(英语:Ordering points to identify the clustering structure)是由Mihael Ankerst,Markus M. Breunig,Hans-Peter Kriegel和Jörg Sander提出的基于密度的聚类分析算法[1]OPTICS并不依赖全局变量来确定聚类,而是将空间上最接近的点相邻排列,以得到数据集合中的对象的线性排序。[2]排序后生成的序列存储了与相邻点之间的距离,并最终生成了一个 dendrogram 。OPTICS算法的思路与DBSCAN类似,但是解决了DBSCAN的一个主要弱点,即如何在密度变化的数据中取得有效的聚类。同时 OPTICS也避免了多数聚类算法中对输入参数敏感的问题。


  1. ^ Ankerst, Mihael; Breunig, Markus M.; Kriegel, Hans-Peter; Sander, Jörg. OPTICS. ACM SIGMOD Record. 1999-06-01, 28 (2): 49–60. ISSN 0163-5808. doi:10.1145/304181.304187. 
  2. ^ OPTICS聚类算法. 知乎专栏. [2018-12-09]. (原始内容存档于2018-12-10) (中文). 
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