python - scikit-learn kmeans 聚类的初始质心

Question

如果我已经有一个可以用作初始质心的 numpy 数组，我该如何正确初始化 kmeans 算法？我正在使用 scikit-learn Kmeans 类

这篇文章 ( k-means with selected initial centers ) 表明如果我使用 numpy 数组作为初始质心，我只需要设置 n_init=1 但我不确定我的初始化是否正常工作

Naftali Harris 出色的可视化页面显示了我正在尝试做的事情 http://www.naftaliharris.com/blog/visualizing-k-means-clustering/

“我会选择”-->“压缩圆”-->运行 kmeans

#numpy array of initial centroids
startpts=np.array([[-0.12, 0.939, 0.321, 0.011], [0.0, 0.874, -0.486, 0.862], [0.0, 1.0, 0.0, 0.033], [0.12, 0.939, 0.321, -0.7], [0.0, 1.0, 0.0, -0.203], [0.12, 0.939, -0.321, 0.25], [0.0, 0.874, 0.486, -0.575], [-0.12, 0.939, -0.321, 0.961]], np.float64)

centroids= sk.KMeans(n_clusters=8, init=startpts, n_init=1)

centroids.fit(actual_data_points)

#get the array
centroids_array=centroids.cluster_centers_

Answer 1

是的，通过 init 设置初始质心应该可行。这是来自 scikit-learn documentation 的引述:

 init : {‘k-means++’, ‘random’ or an ndarray}

     Method for initialization, defaults to ‘k-means++’:   

     If an ndarray is passed, it should be of shape (n_clusters, n_features)
     and gives the initial centers.

---

What is the shape (n_clusters, n_features) referring to?

形状要求意味着init 必须恰好有n_clusters 行，并且每行中的元素数量应该匹配actual_data_points 的维数:

>>> init = np.array([[-0.12, 0.939, 0.321, 0.011],
                     [0.0, 0.874, -0.486, 0.862],
                     [0.0, 1.0, 0.0, 0.033],
                     [0.12, 0.939, 0.321, -0.7],
                     [0.0, 1.0, 0.0, -0.203],
                     [0.12, 0.939, -0.321, 0.25],
                     [0.0, 0.874, 0.486, -0.575],
                     [-0.12, 0.939, -0.321, 0.961]],
                    np.float64)
>>> init.shape[0] == 8  
True  # n_clusters
>>> init.shape[1] == actual_data_points.shape[1]
True  # n_features

What is n_features?

n_features 是样本的维度。例如，如果您要在 2D 平面上聚类点，n_features 将为 2。