将多维集群绘制为 2D 绘图 python

Question

我正在对大量数据进行聚类，这些数据有两个不同的聚类。

第一种类型是 6 维集群，而第二种类型是 12 维集群。现在我决定使用 kmeans（因为它似乎是最直观的聚类算法一开始）。

问题是如何将这些集群映射到二维图上，以便推断 kmeans 是否正常工作。我想用matplotlib，但是任何其他python包都可以。

集群 1 是由这些数据类型 (int,float,float,int,float,int) 组成的集群

Cluster 2 是一个由 12 个浮点类型组成的集群。

试图得到类似这样的输出 任何提示都会很有用。

Answer 1

在搜索互联网并获得许多奇怪的评论较少的解决方案之后。我能够弄清楚该怎么做。如果您尝试做类似的事情，这里是代码。它包含来自各种来源的代码，其中很多是由我编写/编辑的。我希望它比其他人更容易理解。

该函数基于来自 scipy 的 kmeans2，它返回 centroid_list 和 label_list。 kmeansdata 是传递给 kmeans2 进行聚类的 numpy 数组，num_clusters 表示传递给 kmeans2 的集群数量。

代码写回一个新的 png 文件，确保它不会覆盖其他内容。还仅绘制 50 个集群（如果您有 1000 个集群，则不要尝试输出所有集群）

（它是为python2.7编写的，我猜应该也适用于其他版本。）

import numpy
import colorsys
import random
import os
from matplotlib.mlab import PCA as mlabPCA
from matplotlib import pyplot as plt


def get_colors(num_colors):
    """
    Function to generate a list of randomly generated colors
    The function first generates 256 different colors and then
    we randomly select the number of colors required from it
    num_colors        -> Number of colors to generate
    colors            -> Consists of 256 different colors
    random_colors     -> Randomly returns required(num_color) colors
    """
    colors = []
    random_colors = []
    # Generate 256 different colors and choose num_clors randomly
    for i in numpy.arange(0., 360., 360. / 256.):
        hue = i / 360.
        lightness = (50 + numpy.random.rand() * 10) / 100.
        saturation = (90 + numpy.random.rand() * 10) / 100.
        colors.append(colorsys.hls_to_rgb(hue, lightness, saturation))

    for i in range(0, num_colors):
        random_colors.append(colors[random.randint(0, len(colors) - 1)])
    return random_colors


def random_centroid_selector(total_clusters , clusters_plotted):
    """
    Function to generate a list of randomly selected
    centroids to plot on the output png
    total_clusters        -> Total number of clusters
    clusters_plotted      -> Number of clusters to plot
    random_list           -> Contains the index of clusters
                             to be plotted
    """
    random_list = []
    for i in range(0 , clusters_plotted):
        random_list.append(random.randint(0, total_clusters - 1))
    return random_list

def plot_cluster(kmeansdata, centroid_list, label_list , num_cluster):
    """
    Function to convert the n-dimensional cluster to 
    2-dimensional cluster and plotting 50 random clusters
    file%d.png    -> file where the output is stored indexed
                     by first available file index
                     e.g. file1.png , file2.png ...
    """
    mlab_pca = mlabPCA(kmeansdata)
    cutoff = mlab_pca.fracs[1]
    users_2d = mlab_pca.project(kmeansdata, minfrac=cutoff)
    centroids_2d = mlab_pca.project(centroid_list, minfrac=cutoff)


    colors = get_colors(num_cluster)
    plt.figure()
    plt.xlim([users_2d[:, 0].min() - 3, users_2d[:, 0].max() + 3])
    plt.ylim([users_2d[:, 1].min() - 3, users_2d[:, 1].max() + 3])

    # Plotting 50 clusters only for now
    random_list = random_centroid_selector(num_cluster , 50)

    # Plotting only the centroids which were randomly_selected
    # Centroids are represented as a large 'o' marker
    for i, position in enumerate(centroids_2d):
        if i in random_list:
            plt.scatter(centroids_2d[i, 0], centroids_2d[i, 1], marker='o', c=colors[i], s=100)


    # Plotting only the points whose centers were plotted
    # Points are represented as a small '+' marker
    for i, position in enumerate(label_list):
        if position in random_list:
            plt.scatter(users_2d[i, 0], users_2d[i, 1] , marker='+' , c=colors[position])

    filename = "name"
    i = 0
    while True:
        if os.path.isfile(filename + str(i) + ".png") == False:
            #new index found write file and return
            plt.savefig(filename + str(i) + ".png")
            break
        else:
            #Changing index to next number
            i = i + 1
    return

Answer 2

   plot_cluster(X[:], kmean.cluster_centers_, kmean.labels_, clusters)