python学习---python_sklearn层次聚类学习

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分类专栏：可视化机器学习数据可视化文章标签： sklearn python 聚类

于 2022-05-09 07:10:09 首次发布

本文链接：https://blog.youkuaiyun.com/m0_38127487/article/details/124658387

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该示例展示了在2D嵌入的数字数据集上不同层次聚类链接选项的效果，旨在直观地说明度量行为，而非寻找数字的良好聚类。平均链接策略导致少数数据点的几个聚类，而单链链接则产生一个大型聚类，包含大多数数字，以及一个较小的干净聚类，主要由零数字组成，其余聚类来自噪声点。

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An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset.

The goal of this example is to show intuitively how the metrics behave, and not to find good clusters for the digits. This is why the example works on a 2D embedding.

What this example shows us is the behavior “rich getting richer” of agglomerative clustering that tends to create uneven cluster sizes.

This behavior is pronounced for the average linkage strategy, that ends up with a couple of clusters with few datapoints.

The case of single linkage is even more pathologic with a very large cluster covering most digits, an intermediate size (clean) cluster with most zero digits and all other clusters being drawn from noise points around the fringes.

The other linkage strategies lead to more evenly distributed clusters that are therefore likely to be less sensible to a random resampling of the dataset.