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第365行： − + − Cluster analysis is the assignment of a set of observations into subsets (called ''clusters'') so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some ''similarity metric'' and evaluated, for example, by ''internal compactness'', or the similarity between members of the same cluster, and ''separation'', the difference between clusters. Other methods are based on ''estimated density'' and ''graph connectivity''.+ − + − Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity. − − '''聚类分析 Cluster Analysis'''是将一组观测值分配到一个子集（称为集群）中，这样同一个集群中的观测值就可以根据一个或多个预先指定的相似数据点来给定，而从不同的集群中提取的观测值就不一样了。不同的聚类技术对数据的结构会做出不同的假设，通常用一些相似度量来进行定义和评估，例如，通过内部紧凑性，或同一集群成员之间的相似性，以及分离，集群之间的差异''（这里的翻译有待改进）''。也有其他方法是基于密度估计和图连通性来进行相似性度量。
→无监督学习 Unsupervised learning
=====聚类=====
:''主文章：[https://en.wikipedia.org/wiki/Cluster_analysis 聚类分析]''
聚类分析是将一组观测数据分配到子集(称为聚类)中，使同一簇内的观测按照某些预先指定的准则相似，而从不同的簇中提取的观测值则不同。不同的聚类技术对数据的结构提出了不同的假设，通常用某种相似性度量来定义，并通过内部紧密性(同一聚类成员之间的相似性)和不同聚类之间的分离性来评估。其他方法基于估计的密度和图的连通性。聚类是一种[https://en.wikipedia.org/wiki/Unsupervised_learning 无监督学习]方法，是一种常用的[https://en.wikipedia.org/wiki/Statistics 统计][https://en.wikipedia.org/wiki/Data_analysis 数据分析]技术。
==== 半监督学习 Semi-supervised learning ====
==== 半监督学习 Semi-supervised learning ====