4. 基于sklearn的使用 import numpy as np importmatplotlib.pyplot as plt fromsklearn.cluster import KMeans from sklearn import datasets iris = datasets.load_iris() x_train = iris['data'][:,(2, 3)] k = 3 kmeans = KMeans(n_clusters = k,random_state=42) y_pred = kmeans.fit_predi...
# 使用PCA进行降维,以便更好地进行聚类分析pca = PCA(n_components=2) # 降至2维以便可视化 X_pca = pca.fit_transform(X_std) # 使用K-means进行聚类 k = 3 # 基于先前的分析决定将用户分为3个群体 kmeans = KMeans(n_clusters=k, random_state=42) y_kmeans = kmeans.fit_predict(X_pca) #...
Kmeans(n_clusters=4 # 对于指定聚类的簇数,无默认值 ,init="random" # 表示从数据集中随机挑选K个样本点作为初始簇中心 ,n_init=10 # 用于指定该算法运行次数,每次运行时都会选择不同的初始促中心,目的是防止算法收敛于局部最优,默认10 ,max_iter=300 # 表示单次运行的迭代次数,默认300 ,tol=0.0001 # ...
K-Means的惯性计算方式是,每个样本与最接近的集群中心点的均方距离的总和。 kmeans_per_k=[KMeans(n_clusters=k,random_state=42).fit(X) forkinrange(1,10)] inertias=[model.inertia_formodelinkmeans_per_k] plt.figure(figsize=(8,3.5)) plt.plot(range(1,10),inertias,"bo-") plt.xlabel("$k...
# 使用 k-means++ 初始化进行聚类kmeans_pp = KMeans(n_clusters=3, init='k-means++', random_state=42)labels_pp = kmeans_pp.fit_predict(data)centroids_pp = kmeans_pp.cluster_centers_# 数据可视化plt.scatter(data[:, 0], data[:, 1], c=labels_pp, cmap='viridis', marker='o')plt....
random_state=42) y_pred = KMeans(n_clusters=3,random_state=42).fit_predict(X_varied) mglearn.discrete_scatter(X_varied[:,0],X_varied[:,1],y_pred) plt.legend(["cluster0","cluster1","cluster2"],loc='best') plt.xlabel("Feature0") ...
km = KMeans(n_clusters=5, init="k-means++", n_init=10, max_iter=100, random_state=42 ) # 对无离群点数据的聚类 clusters_predict = km.fit_predict(data_no_outliers) 7.4 评价聚类效果 聚类效果如何评价?常用的三种评价指标: Davies-Bouldin指数 ...
我试图在我的数据集中进行聚类,其中有4个数值字段。请查找所附文件:http://www.filedropper.com/example_3.我试过用这个代码:kmeans = KMeans(n_clusters=2, random_state=0, max_iter =300).fit(dffinal) 我知道在这个例子中有两个类,这就是我尝试使用两个集群的原因。在42 浏览0提问于2016-12-23得...
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fromsklearn.clusterimportDBSCANimportnumpyasnpimportmatplotlib.pyplotasplt# 再次使用之前的模拟数据X,_=make_blobs(n_samples=300,centers=4,cluster_std=0.60,random_state=0)# 应用DBSCAN算法dbscan=DBSCAN(eps=0.5,min_samples=5)clusters=dbscan.fit_predict(X)# 绘制聚类结果plt.scatter(X[:,0],X[:,1...