python dbscan聚类算法：如何使用Python的DBSCAN聚类算法进行数据分析-码文网

Python DBSCAN聚类算法是一种基于密度的聚类算法，它可以帮助我们将数据分组，并且可以处理任意形状的数据集。DBSCAN算法的基本思想是：如果一个点的邻域内有足够多的点，那么这个点就是核心点；如果一个点的邻域内没有足够多的点，那么这个点就是噪声点。

是一个简单的Python DBSCAN聚类算法代码：


# 导入所需要的库
import numpy as np
from sklearn.cer import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler
# 生成测试数据
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers, cer_std=0.4,
                            random_state=0)
# 数据标准化
X = StandardScaler().fit_transform(X)
# 运行DBSCAN算法
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_
# 输出结果
n_cers_ = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of cers: %d' % n_cers_)
print("Homogeneity: %0.3f" % metrics.geneity_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))
# 绘制数据和聚类结果
import matplotlib.pyplot as plt
# Black removed and is used for noise instead.
unique_labels = set(labels)
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:
        # Black used for noise.
        col = [0, 0, 0, 1]
    class_member_mask = (labels == k)
    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[
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