ClusterDissimilarityFunction
是 ClusteringTree 和 Dendrogram 的一个选项,指定分群内的相异度.
更多信息

- ClusterDissimilarityFunction 的可能设置是:
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"Single" 最小分群内相异度 "Average" 平均分群内相异度 "Complete" 最大分群内相异度 "WeightedAverage" 加权平均分群内相异度 "Centroid" 与分群中心的距离 "Median" 来自分群中位数的距离 "Ward" Ward 的最小方差相异度 纯函数 - 函数 f 定义任意两个分群之间的距离.
- 函数 f 必须是 DistanceMatrix 的实值函数.
相关指南
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- 聚类分析
文本
Wolfram Research (2016),ClusterDissimilarityFunction,Wolfram 语言函数,https://reference.wolfram.com/language/ref/ClusterDissimilarityFunction.html.
CMS
Wolfram 语言. 2016. "ClusterDissimilarityFunction." Wolfram 语言与系统参考资料中心. Wolfram Research. https://reference.wolfram.com/language/ref/ClusterDissimilarityFunction.html.
APA
Wolfram 语言. (2016). ClusterDissimilarityFunction. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/ClusterDissimilarityFunction.html 年
BibTeX
@misc{reference.wolfram_2025_clusterdissimilarityfunction, author="Wolfram Research", title="{ClusterDissimilarityFunction}", year="2016", howpublished="\url{https://reference.wolfram.com/language/ref/ClusterDissimilarityFunction.html}", note=[Accessed: 15-September-2025]}
BibLaTeX
@online{reference.wolfram_2025_clusterdissimilarityfunction, organization={Wolfram Research}, title={ClusterDissimilarityFunction}, year={2016}, url={https://reference.wolfram.com/language/ref/ClusterDissimilarityFunction.html}, note=[Accessed: 15-September-2025]}