sklearn.cluster.agglomerativeclustering

b'sklearn.cluster.agglomerativeclustering' 是 Scikit-learn 包中用于聚类分析的一种算法，即凝聚聚类算法。它将每个数据点视为单独的聚类，然后在不断合并相似的聚类，直到达到设定的停止条件，例如设定聚类数目或设定距离的阈值等。它适用于有大量数据点和不确定聚类数目的情况。

相关问题

sklearn.cluster.AgglomerativeClustering聚类二维点

AgglomerativeClustering是一种层次聚类算法，可以将数据集划分为不同的类别。在二维点聚类的应用中，可以使用AgglomerativeClustering将二维点聚类为不同的群集。

下面是一个使用AgglomerativeClustering聚类二维点的示例代码：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import AgglomerativeClustering

# 创建一些随机的二维点
np.random.seed(0)
n_samples = 150
X = np.random.randn(n_samples, 2)

# 使用AgglomerativeClustering进行聚类
agg_clustering = AgglomerativeClustering(n_clusters=3)
agg_clustering.fit(X)

# 绘制聚类结果
plt.scatter(X[:, 0], X[:, 1], c=agg_clustering.labels_)
plt.show()

在上面的代码中，我们首先创建了一些随机的二维点，并将它们存储在一个形状为(n_samples, 2)的NumPy数组中。然后，我们使用AgglomerativeClustering算法将这些点聚类为3个不同的群集，并将结果存储在agg_clustering.labels_属性中。最后，我们使用Matplotlib库绘制了聚类结果。

下图显示了上述代码的输出结果：


可以看到，AgglomerativeClustering算法已将二维点聚类为3个不同的群集。每个群集用不同的颜色表示。

sklearn.cluster

Sklearn.cluster is a module in the scikit-learn library that provides various clustering algorithms. Clustering is a technique of grouping similar data points together in such a way that data points in the same group are more similar to each other than to those in other groups. There are many applications of clustering, such as market segmentation, image segmentation, and anomaly detection.

Some of the clustering algorithms provided by sklearn.cluster are:

1. KMeans: It is a popular clustering algorithm that partitions the data into K clusters.

2. AgglomerativeClustering: It is a hierarchical clustering algorithm that starts with each data point as a separate cluster and merges them iteratively based on a linkage criterion.

3. DBSCAN: It is a density-based clustering algorithm that groups together dense regions of data points separated by areas of lower density.

4. SpectralClustering: It is a clustering algorithm that uses graph theory to group together data points that are connected by edges in a graph.

5. Birch: It is a clustering algorithm that incrementally builds a hierarchical clustering tree to cluster the data points.

Sklearn.cluster also provides various metrics to evaluate the quality of the clustering results, such as silhouette score, homogeneity score, completeness score, and adjusted mutual information score.