模型原型
class sklearn.cluster.Agglomerative(n_clusters=2,affinity=’euclidean’,memory=Memory(cachedir=None),connectivity=None, n_components=None,compute_full_tree=’auto’,linkage=’ward’,pooling_func=< function mean >)
参数

• n_clusters
• affinity:用于计算距离（可以为：’euclidean’,’l1’,’l2’,’manhattan’,’cosine’, ‘precomputed’）
• memory:用于缓存输出的结果，默认不缓存
• connectivity:用于连接矩阵，给出了每个样本的可连接样本
• n_components
• compute_full_tree:通常当训练了n_clusters之后，训练过程就停止。但如果compute_full_tree=True，则会继续训练生成一颗完整的树
• linkage:指定链接算法
  
  • ’ward’:单链接single-linkage算法,采用
    dmin
  • ‘compleae’:全链接complete算法,采用
    dmax
  • ‘average’”均链接average-linkage算法,采用
    davg
• pooling_func:输入一组特征值，输出一个数值

属性

• labels_
• n_leaves_:分层树的叶节点数量
• n_components_:连接图中连通分量的估计值
• children_:给出了每个非叶子节点中的子节点数量
  方法
• fit(X[,y])
• fit_predict(X[,y])

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs
from sklearn import cluster
from sklearn.metrics import adjusted_rand_score
from sklearn import mixture

产生数据

def create_data(centers,num=100,std=0.7):
    X,labels_true=make_blobs(n_samples=num,centers=centers,cluster_std=std)
    return X,labels_true

查看生成的样本点

def plot_data(*data):
    X,labels_true=data
    labels=np.unique(labels_true)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    colors='rgbyckm'
    for i,label in enumerate(labels):
        position=labels_true==label
        ax.scatter(X[position,0],X[position,1],label='cluster %d'%label,color=colors[i%len(colors)])
    ax.legend(loc='best',framealpha=0.5)
    ax.set_xlabel('X[0]')
    ax.set_ylabel('Y[1]')
    ax.set_title('data')
    plt.show()

X,labels_true=create_data([[1,1],[2,2],[1,2],[10,20]],1000,0.5)
plot_data(X,labels_true)

使用AgglomerativeClustering

def test_AgglomerativeClustering(*data):
    X,labels_true=data
    clst=cluster.AgglomerativeClustering()
    predicted_labels=clst.fit_predict(X)
    print('ARI:%s'%adjusted_rand_score(labels_true,predicted_labels))

centers=[[1,1],[2,2],[1,2],[10,20]]
X,labels_true=create_data(centers,1000,0.5)
test_AgglomerativeClustering(X,labels_true)

簇的数量的影响

def test_AgglomerativeClustering_nclusters(*data):
    X,labels_true=data
    nums=range(1,50)
    ARIs=[]
    for num in nums:
        clst=cluster.AgglomerativeClustering(n_clusters=num)
        predicted_labels=clst.fit_predict(X)
        ARIs.append(adjusted_rand_score(labels_true,predicted_labels))

    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.plot(nums,ARIs,marker='+')
    ax.set_xlabel('n_clusters')
    ax.set_ylabel('ARI')
    fig.suptitle("AGGlomerativeClustering")
    plt.show()

test_AgglomerativeClustering_nclusters(X,labels_true)

链接方式的影响

def test_AgglomerativeClustering_linkage(*data):
    X,labels_true=data
    nums=range(1,50)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)

    linkages=['ward','complete','average']
    markers='+o*'
    for i,linkage in enumerate(linkages):
        ARIs=[]
        for num in nums:
            clst=cluster.AgglomerativeClustering(n_clusters=num,linkage=linkage)
            predicted_labels=clst.fit_predict(X)
            ARIs.append(adjusted_rand_score(labels_true,predicted_labels))
        ax.plot(nums,ARIs,marker=markers[i],label='linkage:%s'%linkage)

    ax.set_xlabel('n_clusters')
    ax.set_ylabel('ARI')
    ax.legend(loc='best')
    fig.suptitle('AgglomerativeClustering')
    plt.show()

test_AgglomerativeClustering_linkage(X,labels_true)