如何在多维数据上可视化 kmeans 聚类 [英] How to visualize kmeans clustering on multidimensional data

问题描述

我在 mnist 数据集上使用 kmeans 聚类算法，并希望在聚类后可视化图.到目前为止，我做到了

I am using kmeans clustering algorithm on mnist dataset and want to visualize the plots after clustering. So far I did this

from mnist import MNIST

mndata = MNIST('Datasets')
X_train, y_train = mndata.load_training()

#do the clustering
k_means = cluster.KMeans(n_clusters=len(np.unique(y_train)))
k_means.fit(X_train)
labels = k_means.labels_

所以，我现在有 10 个集群代表 0 到 9.我如何可视化这些集群?

So, I now have 10 clusters representing 0 to 9. How can I visualize these clusters?

推荐答案

您可以使用 Pandas 绘图工具 parallel_coordinates 可视化多维聚类.

You can visualise multi-dimensional clustering using pandas plotting tool parallel_coordinates.

predict = k_means.predict(data)
data['cluster'] = predict
pandas.tools.plotting.parallel_coordinates(data, 'cluster')

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