sklearn中的聚集聚类

问题描述

我有一些数据，还有这些数据点的成对距离矩阵.我想使用聚集聚类对它们进行聚类.我读到在sklearn中，我们可以将预先计算"为亲和力，我希望它是距离矩阵.但是我找不到任何使用预先计算的亲和力和自定义距离矩阵的示例.任何帮助将不胜感激.

解决方案

我们将距离矩阵称为D.

agg = AgglomerativeClustering(n_clusters=5, affinity='precomputed', linkage = 'average')
agg.fit_predict(D)  # Returns class labels.

如果您有兴趣生成整个层次结构并生成树状图，则scikit-learn的API会包装 scipy分层群集代码.只需直接使用scipy代码即可.

I have some data and also the pairwise distance matrix of these data points. I want to cluster them using Agglomerative clustering. I readthat in sklearn, we can have 'precomputed' as affinity and I expect it is the distance matrix. But I could not find any example which uses precomputed affinity and a custom distance matrix.Any help will be appreciated.

解决方案

Let's call your distance matrix D.

agg = AgglomerativeClustering(n_clusters=5, affinity='precomputed', linkage = 'average')
agg.fit_predict(D)  # Returns class labels.

If you're interested in generating the entire hierarchy and producing a dendrogram, scikit-learn's API wraps the scipy hierarchical clustering code. Just use the scipy code directly.

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