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The choice of an appropriate metric will influence the shape of the clusters, as some elements may be close to one another according to one distance and farther away according to another. For example, in a 2-dimensional space, the distance between the point (1,0) and the origin (0,0) is always 1 according to the usual norms, but the distance between the point (1,1) and the origin (0,0) can be 2 under Manhattan distance, <math>\scriptstyle\sqrt{2}</math> under Euclidean distance, or 1 under maximum distance.
The choice of an appropriate metric will influence the shape of the clusters, as some elements may be close to one another according to one distance and farther away according to another. For example, in a 2-dimensional space, the distance between the point (1,0) and the origin (0,0) is always 1 according to the usual norms, but the distance between the point (1,1) and the origin (0,0) can be 2 under Manhattan distance, <math>\scriptstyle\sqrt{2}</math> under Euclidean distance, or 1 under maximum distance.
度量方式的选择将影响数据簇类的形状，因为某些元素依据一个距离可能彼此接近，而依据另一个距离可能彼此远离。例如，在一个二维空间中，点(1,0)和原点(0,0)之间的距离通常是1，但是点(1,1)和原点(0,0)之间的距离在曼哈顿距离下可以是2，在欧几里得度量下可以是1，在最大距离下可以是1。
For text or other non-numeric data, metrics such as the Hamming distance or Levenshtein distance are often used.
For text or other non-numeric data, metrics such as the Hamming distance or Levenshtein distance are often used.
对于文本文字或其他非数字数据，常常使用汉明距离或莱文斯坦距离等度量标准。
A review of cluster analysis in health psychology research found that the most common distance measure in published studies in that research area is the Euclidean distance or the squared Euclidean distance.
A review of cluster analysis in health psychology research found that the most common distance measure in published studies in that research area is the Euclidean distance or the squared Euclidean distance.
通过对数据聚类健康心理学研究的回顾发现，在该研究领域已发表的研究中，最常见的距离测量方法是欧几里得度量距离或欧几里得度量距离的平方。
=== Linkage criteria 连接准则===
=== Linkage criteria 连接准则===