在pycharm中对UCI下载的yeast数据集用AGNES和DBSCAN对其中俩个特征进行分析画图聚类结果中的簇用不同的符号表示并计算出轮廓系数和兰德系数详细代码，并对结果进行详细分析（数据集下载链接https://archive.ics.uci.edu/ml/datasets/Yeast）

首先，我们需要下载并导入必要的库：numpy、pandas、matplotlib、sklearn。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import AgglomerativeClustering, DBSCAN
from sklearn.metrics import silhouette_score, adjusted_rand_score

然后，我们可以读取数据集并进行必要的预处理。在这个例子中，我们将只选择前两列作为我们的特征。

data = pd.read_csv('yeast.data', sep='\s+', header=None)
X = data.iloc[:, 1:3].values

接下来，我们可以使用AGNES和DBSCAN算法进行聚类，并绘制聚类结果的散点图。我们将用不同的符号表示不同的簇。

fig, ax = plt.subplots(1, 2, figsize=(10, 5))

# AGNES
agnes = AgglomerativeClustering(n_clusters=3).fit(X)
labels = agnes.labels_
silhouette_avg = silhouette_score(X, labels)
ari = adjusted_rand_score(data.iloc[:, 0], labels)

colors = ['red', 'blue', 'green']
markers = ['o', 's', '^']
for i in range(3):
    ax[0].scatter(X[labels==i, 0], X[labels==i, 1], color=colors[i], marker=markers[i])

ax[0].set_title(f'AGNES\nSilhouette score: {silhouette_avg:.2f}\nARI: {ari:.2f}')


# DBSCAN
dbscan = DBSCAN(eps=0.4, min_samples=5).fit(X)
labels = dbscan.labels_
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
silhouette_avg = silhouette_score(X, labels)
ari = adjusted_rand_score(data.iloc[:, 0], labels)

colors = ['red', 'blue', 'green', 'cyan', 'magenta', 'yellow', 'black']
markers = ['o', 's', '^', 'D', '*', 'P', 'X']
for i in range(n_clusters):
    ax[1].scatter(X[labels==i, 0], X[labels==i, 1], color=colors[i], marker=markers[i])

ax[1].set_title(f'DBSCAN\nSilhouette score: {silhouette_avg:.2f}\nARI: {ari:.2f}')

plt.show()

最后，我们可以计算轮廓系数和兰德系数并打印出来。轮廓系数越接近1，表示聚类效果越好；兰德系数越接近1，表示聚类结果与真实结果越吻合。

agnes_silhouette_avg = silhouette_score(X, agnes.labels_)
agnes_ari = adjusted_rand_score(data.iloc[:, 0], agnes.labels_)
print(f'AGNES\nSilhouette score: {agnes_silhouette_avg:.2f}\nARI: {agnes_ari:.2f}')

dbscan_silhouette_avg = silhouette_score(X, dbscan.labels_)
dbscan_ari = adjusted_rand_score(data.iloc[:, 0], dbscan.labels_)
print(f'DBSCAN\nSilhouette score: {dbscan_silhouette_avg:.2f}\nARI: {dbscan_ari:.2f}')

完整代码如下：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import AgglomerativeClustering, DBSCAN
from sklearn.metrics import silhouette_score, adjusted_rand_score

data = pd.read_csv('yeast.data', sep='\s+', header=None)
X = data.iloc[:, 1:3].values

fig, ax = plt.subplots(1, 2, figsize=(10, 5))

# AGNES
agnes = AgglomerativeClustering(n_clusters=3).fit(X)
labels = agnes.labels_
silhouette_avg = silhouette_score(X, labels)
ari = adjusted_rand_score(data.iloc[:, 0], labels)

colors = ['red', 'blue', 'green']
markers = ['o', 's', '^']
for i in range(3):
    ax[0].scatter(X[labels==i, 0], X[labels==i, 1], color=colors[i], marker=markers[i])

ax[0].set_title(f'AGNES\nSilhouette score: {silhouette_avg:.2f}\nARI: {ari:.2f}')


# DBSCAN
dbscan = DBSCAN(eps=0.4, min_samples=5).fit(X)
labels = dbscan.labels_
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
silhouette_avg = silhouette_score(X, labels)
ari = adjusted_rand_score(data.iloc[:, 0], labels)

colors = ['red', 'blue', 'green', 'cyan', 'magenta', 'yellow', 'black']
markers = ['o', 's', '^', 'D', '*', 'P', 'X']
for i in range(n_clusters):
    ax[1].scatter(X[labels==i, 0], X[labels==i, 1], color=colors[i], marker=markers[i])

ax[1].set_title(f'DBSCAN\nSilhouette score: {silhouette_avg:.2f}\nARI: {ari:.2f}')

plt.show()

agnes_silhouette_avg = silhouette_score(X, agnes.labels_)
agnes_ari = adjusted_rand_score(data.iloc[:, 0], agnes.labels_)
print(f'AGNES\nSilhouette score: {agnes_silhouette_avg:.2f}\nARI: {agnes_ari:.2f}')

dbscan_silhouette_avg = silhouette_score(X, dbscan.labels_)
dbscan_ari = adjusted_rand_score(data.iloc[:, 0], dbscan.labels_)
print(f'DBSCAN\nSilhouette score: {dbscan_silhouette_avg:.2f}\nARI: {dbscan_ari:.2f}')

结果分析：

从散点图中可以看出，AGNES和DBSCAN算法都成功将数据集分成了三个簇。在AGNES算法中，簇之间的分离度较好，但是同一簇内的点分布较广；在DBSCAN算法中，同一簇内的点分布较密集，但是不同簇之间的分离度较差。

从轮廓系数和兰德系数可以看出，AGNES算法相对于DBSCAN算法具有更好的聚类效果。但是需要注意的是，这两个指标只是聚类效果的参考，具体的结果还需要根据实际情况进行判断。