Scikit-learn：使用 DBSCAN 进行聚类后，绘制的点比初始数据样本少

Posted 2023-03-12

【中文标题】Scikit-learn：使用 DBSCAN 进行聚类后，绘制的点比初始数据样本少

【英文标题】：Scikit-learn: Less points plotted than initial data samples after clustering with DBSCAN

【发布时间】：2018-12-09 23:33:34

【问题描述】：

我在使用库 scikit-learn 中的 DBSCAN 实现时，发现绘制的点数低于初始样本数。 特别是在DBSCANhttp://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html的官方demo中，自动生成了750个样本。但是，当我打印每个集群有多少点以及有多少异常值时，结果是： 集群 1：224， 第 2 组：228， 第 3 组：227， 异常值：18， --> TOTAL = 697。从下面的代码可以看出，我只是在原始代码中添加了几行，用于打印每个聚类的点数和异常值数。我对此感到困惑，我想知道为什么会发生这种情况以及缺失点在哪里。 提前感谢您的回答！

print(__doc__)

import numpy as np

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler


# #############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
                            random_state=0)


X = StandardScaler().fit_transform(X)

# #############################################################################
# Compute DBSCAN
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_

n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)



print('Estimated number of clusters: %d' % n_clusters_)
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
print("Adjusted Rand Index: %0.3f"
      % metrics.adjusted_rand_score(labels_true, labels))
print("Adjusted Mutual Information: %0.3f"
      % metrics.adjusted_mutual_info_score(labels_true, labels))
print("Silhouette Coefficient: %0.3f"
      % metrics.silhouette_score(X, labels))

# #############################################################################
# Plot result
import matplotlib.pyplot as plt


unique_labels = set(labels)

i=1
for k in zip(unique_labels):

      class_member_mask = (labels == k)

      if k == (-1,):
        xy = X[class_member_mask & ~core_samples_mask]
        current_outliers = len(xy)
        print "OUTLIERS :", current_outliers
      else:
        xy = X[class_member_mask & core_samples_mask]
        print "CLUSTER", i, " :",len(xy)
      i+=1 

colors = [plt.cm.Spectral(each)
          for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
    if k == -1:`enter code here`
        col = [0, 0, 0, 1]

    class_member_mask = (labels == k)

    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=14)

    xy = X[class_member_mask & ~core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=6)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

【参考方案1】：

您的情节中仅包含core samples。如果您想考虑所有点，请删除core_samples_mask 上的约束：

  if k == (-1,):
    xy = X[class_member_mask]
    current_outliers = len(xy)
    print "OUTLIERS :", current_outliers
  else:
    xy = X[class_member_mask]
    print "CLUSTER", i, " :",len(xy)
  i+=1