有针对我的具体情况的聚类算法吗？

问题描述

这是我的数据的散点图：

我认为，我在这里看到两个集群。顶部较大，较宽，密度较高的一个，而底部较右下部较小，密度较低的一个：

当我进行k均值聚类时，数据最终会在中间被拆分：

我的问题：是否有一种聚类算法可以识别我正在查看/描述的聚类？

解决方法

我在这里看不到任何代码或数据。无论如何，我建议您使用DBSCAN，Affinity Propagation，也可以尝试使用GMM。您可以在下面找到一些示例代码。

https://scikit-learn.org/stable/modules/clustering.html#dbscan

https://scikit-learn.org/stable/modules/clustering.html#affinity-propagation

https://scikit-learn.org/stable/modules/mixture.html#mixture

print(__doc__)

import numpy as np

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler


# #############################################################################
# Generate sample data
centers = [[1,1],[-1,-1],[1,-1]]
X,labels_true = make_blobs(n_samples=750,centers=centers,cluster_std=0.4,random_state=0)

X = StandardScaler().fit_transform(X)

# #############################################################################
# Compute DBSCAN
db = DBSCAN(eps=0.3,min_samples=10).fit(X)
core_samples_mask = np.zeros_like(db.labels_,dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_

# Number of clusters in labels,ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)

print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)
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))

# #############################################################################
# Plot result
import matplotlib.pyplot as plt

# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = [plt.cm.Spectral(each)
          for each in np.linspace(0,1,len(unique_labels))]
for k,col in zip(unique_labels,colors):
    if k == -1:
        # Black used for noise.
        col = [0,1]

    class_member_mask = (labels == k)

    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:,0],xy[:,'o',markerfacecolor=tuple(col),markeredgecolor='k',markersize=14)

    xy = X[class_member_mask & ~core_samples_mask]
    plt.plot(xy[:,markersize=6)

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