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第475行：
第475行： − 无监督式学习算法只需要一组只包含输入的数据，然后在数据中找到结构，比如对数据点进行分组或聚类。因此，算法从未被标记、分类或分类的测试数据中学习。而不是响应反馈，非监督式学习算法识别数据中的共性，并根据每个新数据中是否存在这些共性而做出反应。非监督式学习的一个核心应用是统计学中的密度估计领域，比如寻找概率密度函数。尽管非监督式学习包含了其他领域，包括总结和解释数据特性。+ 第483行：
第483行： − 数据聚类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标准相似，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做出不同的假设，通常用一些相似度量来定义和评估，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。其他方法是基于密度估计和图连通性。+ − −
→无监督学习 Unsupervised learning
Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features.
Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features.
无监督式学习算法只需要一组只包含输入的数据，通过寻找数据中潜在结构、规律，对数据点进行分组或聚类。因此，算法是从未被标记、分类或分类的测试数据中学习，而不是通过响应反馈来改进策略。无监督式学习算法可以识别数据中的共性，并根据每个新数据中是否存在这些共性而做出反应。无监督学习的一个核心应用是统计学中的密度估计领域，比如寻找概率密度函数。尽管非监督式学习也包含了其他领域，如总结和解释数据特性。
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。
==== 半监督学习 Semi-supervised learning ====
==== 半监督学习 Semi-supervised learning ====