簇内相似度 Kmeans

Question

我正在尝试在 sklearn python 中使用 kmeans 对二维用户数据进行聚类。我使用肘部方法（簇号的增加不会导致平方误差和显着下降的点）来识别正确的号。簇数为 50。

Post 应用 kmeans，我想了解每个集群内数据点的相似性。因为我有 50 个集群，有没有办法得到一个数字（类似于每个集群中的方差），这可以帮助我了解每个集群中的数据点有多接近。像 0.8 这样的数字意味着记录在每个集群中具有高方差，而 0.2 意味着它们接近 "related".

总而言之，有没有什么方法可以得到一个数字来识别 kmeans 中的每个簇 "good" 是怎样的？我们可以说好是相对的，但让我们考虑一下，我对集群内方差更感兴趣，以确定特定集群有多好。

Answer 1

使用从 https://plot.ly/scikit-learn/plot-kmeans-silhouette-analysis/

中获取的剪影得分的代码示例

from __future__ import print_function

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score

# Generating the sample data from make_blobs
# This particular setting has one distinct cluster and 3 clusters placed close
# together.
X, y = make_blobs(n_samples=500,
                  n_features=2,
                  centers=4,
                  cluster_std=1,
                  center_box=(-10.0, 10.0),
                  shuffle=True,
                  random_state=1)  # For reproducibility

range_n_clusters = [2, 3, 4, 5, 6]

for n_clusters in range_n_clusters:
  # Initialize the clusterer with n_clusters value and a random generator
  # seed of 10 for reproducibility.
  clusterer = KMeans(n_clusters=n_clusters, random_state=10)
  cluster_labels = clusterer.fit_predict(X)
  print(cluster_labels)
  # The silhouette_score gives the average value for all the samples.
  # This gives a perspective into the density and separation of the formed
  # clusters
  silhouette_avg = silhouette_score(X, cluster_labels)
  print("For n_clusters =", n_clusters,
        "The average silhouette_score is :", silhouette_avg)

  # Compute the silhouette scores for each sample
  sample_silhouette_values = silhouette_samples(X, cluster_labels)

簇内相似度 Kmeans

Within Cluster Similarity Kmeans

python

cluster-analysis

k-means