Whether using an agglomerative method or a divisive method, a core need is to measure the distance between two clusters, 
where each cluster is a set of objects. 不管是聚合还是分歧，核心操作都需要计算不同聚类之间的距离。
- Single link (minimum distance, nearest-neighbour clustering): smallest distance between an element in one cluster and an element in the other 单个链接(最小距离，最近邻聚类):一个聚类中的元素和另一个聚类中的元素之间的最小距离
- i.e.,
- i.e.,
- Complete link (maximum distance): largest distance between an element in one cluster and an element in the other 完整链接(最大距离):一个群集中的元素与另一个群集中的元素之间的最大距离
- i.e.,
- i.e.,
- Average (average distance): average distance between an element in one cluster and an element in the other 平均(平均距离):一个簇中的元素和另一个簇中的元素之间的平均距离
- i.e.,
- i.e.,
- Centroid: distance between the centroids of two clusters 质心:两个簇的质心之间的距离
- i.e.,
- i.e.,
- Medoid: distance between the medoids of two clusters
- i.e.,
- i.e.,
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