Clustering- DBSCAN
These codes are imported from Scikit-Learn python package for learning purpose
import matplotlib.pyplot as plt import numpy as np import seaborn as sns %matplotlib inline sns.set()
8. Demo of DBSCAN clustering algorithm
Finds core samples of high density and expands clusters from them.
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)
Preprocessing
X = StandardScaler().fit_transform(X)
Compute DBSCAN
# 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_
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))
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
Results
# Black removed and is used for noise instead. unique_labels = set(labels) plt.figure(figsize = [12,10]) 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()