标签：Web DBSCAN db 0.3 labels mask samples print world

本小节通过生成的聚类数据集，使用DBSCAN方法进行分类，并将其可视化。

数据集的生成

    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)

运行DBSCAN算法，配置eps=0.3，最小样本=10

    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_

完整源码如下

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
import matplotlib.pyplot as plt

def show_dbscan():
    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)
    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_

    # Number of clusters in labels, ignoring noise if present.
    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))
    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = 'k'

        class_member_mask = (labels == k)

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

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

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

if __name__ == '__main__':
    print("Hello World!")
    show_dbscan()

运行结果

Hello World!
Estimated number of clusters: 3
Homogeneity: 0.953
Completeness: 0.883
V-measure: 0.917
Adjusted Rand Index: 0.952
Adjusted Mutual Information: 0.883
Silhouette Coefficient: 0.626

可视化

 本小节只是讲解如何使用dbscan，不需要知道事先形成簇的数量，本章即结束。

标签：Web,DBSCAN,db,0.3,labels,mask,samples,print,world
来源： https://blog.csdn.net/mooyuan/article/details/122763287

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