Affinity Propagation首选项初始化

Question

我需要在不事先知道集群数量的情况下执行集群.簇的数量可以是1到5,因为我可以找到所有样本属于同一实例或有限数量的组的情况.我认为亲和力传播可能是我的选择,因为我可以通过设置首选项参数来控制群集的数量.但是,如果我有人工生成的单个集群,并且我将节点之间的最小欧氏距离设置为偏好(以最小化集群数量),那么我对集群的处理非常糟糕.

"""
=================================================
Demo of affinity propagation clustering algorithm
=================================================

Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007

"""
print(__doc__)
import numpy as np
from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from scipy.spatial.distance import pdist

##############################################################################
# Generate sample data
centers = [[0,0],[1,1]]
X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5,
                            random_state=0)
init = np.min(pdist(X))

##############################################################################
# Compute Affinity Propagation
af = AffinityPropagation(preference=init).fit(X)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_

n_clusters_ = len(cluster_centers_indices)

print('Estimated number of clusters: %d' % n_clusters_)
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
print("Adjusted Rand Index: %0.3f"
      % metrics.adjusted_rand_score(labels_true, labels))
print("Adjusted Mutual Information: %0.3f"
      % metrics.adjusted_mutual_info_score(labels_true, labels))
print("Silhouette Coefficient: %0.3f"
      % metrics.silhouette_score(X, labels, metric='sqeuclidean'))

##############################################################################
# Plot result
import matplotlib.pyplot as plt
from itertools import cycle

plt.close('all')
plt.figure(1)
plt.clf()

colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for k, col in zip(range(n_clusters_), colors):
    class_members = labels == k
    cluster_center = X[cluster_centers_indices[k]]
    plt.plot(X[class_members, 0], X[class_members, 1], col + '.')
    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
             markeredgecolor='k', markersize=14)
    for x in X[class_members]:
        plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

我使用亲和传播的方法有什么缺陷吗？相反,Affinity Propagation不适用于此任务,我应该使用其他东西吗？

Answer 1

不,没有缺陷.AP不使用距离,但要求您指定相似性.我不太了解scikit实现,但根据我所读到的,它默认使用负平方欧几里德距离来计算相似度矩阵.如果将输入首选项设置为最小欧几里德距离,则得到正值,而所有相似度均为负值.因此,这通常会产生与样本一样多的聚类(注意:输入首选项越高,聚类越多).我宁愿建议将输入偏好设置为最小负平方距离,即-1的平方最大数据集中的距离.这将为您提供更少数量的集群,但不一定是一个集群.我不知道是否在scikit实现中也存在preferenceRange()函数.在AP主页上有Matlab代码,它也在我正在维护的R包'apcluster'中实现.此函数允许确定输入首选项参数的有意义边界.我希望有所帮助.