在Python中，可以使用scikit-learn库中的`KMeans`类来轻松实现K-均值聚类算法。以下是一个简单的示例代码：```python from sklearn.cluster import KMeans import numpy as np from sklearn.datasets import make_blobs # 生成模拟数据 X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, ran...
K-Means这个词第一次使用是在1967,但是它的思想可以追溯到1957年,它是一种非常简单地基于距离的聚类算法,认为每个Cluster由相似的点组成而这种相似性由距离来衡量,不同Cluster间的点应该尽量不相似,每个Cluster都会有一个“重心”;另外它也是一种排他的算法,即任意点必然属于某一Cluster且只属于该Cluster。当然它的...
from sklearn.cluster import KMeans # 进行K-Means聚类 kmeans = KMeans(n_clusters=3) df_pca['cluster'] = kmeans.fit_predict(df_scaled) 可视化结果 # 使用散点图可视化PCA后的数据 plt.figure(figsize=(12, 6)) plt.scatter(df_pca['PC1'], df_pca['PC2'], c=df_pca['cluster'], cmap=...
# 0.引入依赖importnumpyasnpimportmatplotlib.pyplotasplt#画图#从sklearn中直接生成聚类数据fromsklearn.datasetsimportmake_blobs# 1.数据加载#n_samples样本点的个数,centers中心点的个数,random_state随机种子,cluster_std聚类的标准差(随机分布的偏差大小)x,y=make_blobs(n_samples=100,centers=6,random...
聚类:K-means Cluster: Cluster Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis:根据数据的特征找出数据间的相似性,将相似的数据分成一个类。 Unsupervised learning: no predefined classes...
example [idx,C] = kmeans(___) returns the k cluster centroid locations in the k-by-p matrix C. example [idx,C,sumd] = kmeans(___) returns the within-cluster sums of point-to-centroid distances in the k-by-1 vector sumd. example [idx,C,sumd,D] = kmeans(___) returns dis...
from sklearn.cluster import KMeans import numpy as np from sklearn.datasets import make_blobs X, y = make_blobs(n_samples=500,n_features=2,centers=4,random_state=0) clu=KMeans(n_clusters=4,random_state=10).fit(X) # 聚类结果的每个样本的标签 ...
print("Cluster Centers: ") forcenterincenters: print(center) # $example off$ spark.stop() ''' sample_kmeans_data.txt 0 1:0.0 2:0.0 3:0.0 1 1:0.1 2:0.1 3:0.1 2 1:0.2 2:0.2 3:0.2 3 1:9.0 2:9.0 3:9.0 4 1:9.1 2:9.1 3:9.1 ...
KMeans is used to cluster the data into groups for further analysis and to test the theory. You can find out more about KMeans on Wikipedia Wikipedia KMeans .The data that we are going to use in today's example is stock market data with the ConnorsRSI indicator. You can learn ...
print(kmeans.labels_)print(kmeans.labels_.shape) # Predicting the cluster of an incoming new data point sample_test = np.array([-3, -3]) print(sample_test) test = sample_test.reshape(1, -1) print(test) pred = kmeans.predict(test) ...