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→簇异性 Cluster dissimilarity
=== 连接准则 Linkage criteria===
=== 连接准则 Linkage criteria===
连接标准决定了集合内簇之间的距离作为两个簇之间距离的函数。簇''A''和簇''B''之间一些常用的连接标准如下：<ref>{{cite web | title=The CLUSTER Procedure: Clustering Methods |url=https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/statug_cluster_sect012.htm | work=SAS/STAT 9.2 Users Guide | publisher= [[SAS Institute]] | date= | accessdate=2009-04-26}}</ref><ref>{{cite journal |last=Székely |first=G. J. |last2=Rizzo |first2=M. L. |year=2005 |title=Hierarchical clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method |journal=Journal of Classification |volume=22 |issue=2 |pages=151–183 |doi=10.1007/s00357-005-0012-9 }}</ref>
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* 在合并了两个簇之后，某个簇的定义符号（即为度量一个簇的质量而定义的一个量）的增量。<ref>Zhang, et al. "Agglomerative clustering via maximum incremental path integral." Pattern Recognition (2013).</ref><ref>Zhao, and Tang. "Cyclizing clusters via zeta function of a graph."Advances in Neural Information Processing Systems. 2008.</ref><ref>Ma, et al. "Segmentation of multivariate mixed data via lossy data coding and compression." IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9) (2007): 1546-1562.</ref>
* 在合并了两个簇之后，某个簇的定义符号（即为度量一个簇的质量而定义的一个量）的增量。<ref>Zhang, et al. "Agglomerative clustering via maximum incremental path integral." Pattern Recognition (2013).</ref><ref>Zhao, and Tang. "Cyclizing clusters via zeta function of a graph."Advances in Neural Information Processing Systems. 2008.</ref><ref>Ma, et al. "Segmentation of multivariate mixed data via lossy data coding and compression." IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9) (2007): 1546-1562.</ref>
==讨论==
==讨论==