sklearn preprocessing

2.

from sklearn.cluster import DBSCAN

y_pred = DBSCAN().fit_predict(X)

plt.scatter(X[:, 0], X[:, 1], c=y_pred)

plt.show()

调参:调参增加类别，有两个方向都是可以的，一个是继续减少eps(默认0.5)，另一个是增加min_samples(默认5)

3.

from sklearn.cluster import KMeans

y_pred = KMeans(n_clusters=3, random_state=9).fit_predict(X)

plt.scatter(X[:, 0], X[:, 1], c=y_pred)

plt.show()

4.评估

from sklearn import metrics

metrics.calinski_harabaz_score(X, y_pred)