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其中 ''d'' 是选定的度量单位。其他连接准则包括:

其中 ''d'' 是选定的度量单位。其他连接准则包括：

* 簇内方差之和。

* 簇内方差之和。

* 被合并的簇的方差增量（[[Ward标准]]）<ref name="wards method">{{cite journal|doi=10.2307/2282967|last=Ward |first=Joe H. |title=Hierarchical Grouping to Optimize an Objective Function |journal=Journal of the American Statistical Association |volume=58 |issue=301 |year=1963 |pages=236–244}}</ref>.

* 被合并的簇的方差增量（[[Ward标准]]）<ref name="wards method">{{cite journal|doi=10.2307/2282967|last=Ward |first=Joe H. |title=Hierarchical Grouping to Optimize an Objective Function |journal=Journal of the American Statistical Association |volume=58 |issue=301 |year=1963 |pages=236–244}}</ref>。

* 候选簇从同一分布函数（'''<font color="#ff8000">V-连接 V-linkage</font>'''）衍生的概率。

* 候选簇从同一分布函数（'''<font color="#ff8000">V-连接 V-linkage</font>'''）衍生的概率。

* [[k-最近邻  k-nearest-neighbour]]图 （KNN，图度连接）上的入度与出度的乘积<ref>{{Cite journal|last=Zhang|first=Wei|last2=Wang|first2=Xiaogang|last3=Zhao|first3=Deli|last4=Tang|first4=Xiaoou|date=2012|editor-last=Fitzgibbon|editor-first=Andrew|editor2-last=Lazebnik|editor2-first=Svetlana|editor3-last=Perona|editor3-first=Pietro|editor4-last=Sato|editor4-first=Yoichi|editor5-last=Schmid|editor5-first=Cordelia|title=Graph Degree Linkage: Agglomerative Clustering on a Directed Graph|journal=Computer Vision – ECCV 2012|series=Lecture Notes in Computer Science|language=en|publisher=Springer Berlin Heidelberg|volume=7572|pages=428–441|doi=10.1007/978-3-642-33718-5_31|isbn=9783642337185|arxiv=1208.5092|bibcode=2012arXiv1208.5092Z}} 见： https://github.com/waynezhanghk/gacluster</ref>

* [[k-最近邻  k-nearest-neighbour]]图 （KNN，图度连接）上的入度与出度的乘积<ref>{{Cite journal|last=Zhang|first=Wei|last2=Wang|first2=Xiaogang|last3=Zhao|first3=Deli|last4=Tang|first4=Xiaoou|date=2012|editor-last=Fitzgibbon|editor-first=Andrew|editor2-last=Lazebnik|editor2-first=Svetlana|editor3-last=Perona|editor3-first=Pietro|editor4-last=Sato|editor4-first=Yoichi|editor5-last=Schmid|editor5-first=Cordelia|title=Graph Degree Linkage: Agglomerative Clustering on a Directed Graph|journal=Computer Vision – ECCV 2012|series=Lecture Notes in Computer Science|language=en|publisher=Springer Berlin Heidelberg|volume=7572|pages=428–441|doi=10.1007/978-3-642-33718-5_31|isbn=9783642337185|arxiv=1208.5092|bibcode=2012arXiv1208.5092Z}} 见： https://github.com/waynezhanghk/gacluster</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>

* 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>。

 

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==讨论==

==讨论==