我正在尝试在此.csv 上运行 DBSCAN 算法。在我的程序的第一部分，我加载它并在其中绘制数据以检查其分布。这是代码的第一部分：

import csv
import sys
import os
from os.path import join
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt
import numpy as np

def load_data(file_path, file_name):
   with open(join(file_path, file_name)) as csv_file:
       data_file = csv.reader(csv_file,delimiter=',')
       temp1 = next(data_file)
       n_samples = int(temp1[0])
       print("n_samples=")
       print(n_samples)
       n_features = int(temp1[1])
       temp2 = next(data_file)
       feature_names = np.array(temp2[:n_features])

       data_list = [iter for iter in data_file]
               
       data = np.asarray(data_list, dtype=np.float64)                  
       
   return(data,feature_names,n_samples,n_features)

# --- Main program ---

file_path="Datasets/"    
file_name3="CURE-complete.csv"    
data3,feature_names3,n_samples3,n_features3 = load_data(file_path, file_name3)
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
fig.subplots_adjust(top=1)
ax.set_title('Dataset n. 3 of data points')
ax.set_xlabel(feature_names3[0])
ax.set_ylabel(feature_names3[1])
plt.plot(data3[:,0], data3[:,1], '.', markersize=1.2, markeredgecolor = 'blue')
plt.show()

这是数据的表示方式：

因为我需要知道我应该将数据集分成多少个集群，所以我使用了返回 4 的 Means 算法（这是图表的肘部，最佳值）：

sse = {}
for k in range(1, 11):
    kmeans = KMeans(n_clusters=k, random_state=0).fit(data3)
    sse[k] = kmeans.inertia_
    
kn = KneeLocator(
    list(sse.keys()),
    list(sse.values()),
    curve='convex',
    direction='decreasing',
    interp_method='polynomial',
)

plt.figure()
plt.plot(list(sse.keys()), list(sse.values()))
plt.vlines(kn.knee, plt.ylim()[0], plt.ylim()[1], linestyles='dashed')
plt.xlabel("Number of clusters")
plt.ylabel("SSE")
plt.show()

这是图表：

然后，我使用以下参数运行 DBSCAN 算法：min_sample = 10（固定值，我需要使用它）和 eps = 2。这是代码：

np.random.seed(5)
dbscan2 = DBSCAN(eps=2, min_samples=10).fit(data3)
fig = plt.figure(figsize=(20,10))

ax = fig.add_subplot(111)
fig.subplots_adjust(top=1)
ax.set_title('Clustered points in dataset n. 3')

ax.set_xlabel('x')
ax.set_ylabel('y')

# set the list of colors to be selected when plotting the different clusters
color=['b','g','r','c','m','y','k','w']

# number of clusters given by the diagram
k=4

#plot the dataset
for clu in range(k):
    # collect the sequence of cooordinates of the points in each given cluster (determined by clu)
    data_list_x = [data3[i,0] for i in range(n_samples3) if dbscan2.labels_[i]==clu]
    data_list_y = [data3[i,1] for i in range(n_samples3) if dbscan2.labels_[i]==clu]
    plt.scatter(data_list_x, data_list_y, s=10, edgecolors='none', c=color[clu], alpha=0.5)

plt.show()

这是结果：

我的流程不正确吗？我不明白为什么情节看起来如此不同。我希望有一个与第一个类似的图表，但每个集群的颜色不同。