当前位置：首页 > article >正文

系统聚类的分类数确定——聚合系数法

article 2024/11/7 7:03:21

breast_cancer数据集分析——乳腺癌诊断

#读取乳腺癌数据
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
X = data.data
y = data.target

.. _breast_cancer_dataset:

Breast cancer wisconsin (diagnostic) dataset
--------------------------------------------

**Data Set Characteristics:**

:Number of Instances: 569

:Number of Attributes: 30 numeric, predictive attributes and the class

:Attribute Information:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension ("coastline approximation" - 1)

The mean, standard error, and "worst" or largest (mean of the three
worst/largest values) of these features were computed for each image,
resulting in 30 features. For instance, field 0 is Mean Radius, field
10 is Radius SE, field 20 is Worst Radius.

- class:
- WDBC-Malignant
- WDBC-Benign

:Summary Statistics:

===================================== ====== ======
Min Max
===================================== ====== ======
radius (mean): 6.981 28.11
texture (mean): 9.71 39.28
perimeter (mean): 43.79 188.5
area (mean): 143.5 2501.0
smoothness (mean): 0.053 0.163
compactness (mean): 0.019 0.345
concavity (mean): 0.0 0.427
concave points (mean): 0.0 0.201
symmetry (mean): 0.106 0.304
fractal dimension (mean): 0.05 0.097
radius (standard error): 0.112 2.873
texture (standard error): 0.36 4.885
perimeter (standard error): 0.757 21.98
area (standard error): 6.802 542.2
smoothness (standard error): 0.002 0.031
compactness (standard error): 0.002 0.135
concavity (standard error): 0.0 0.396
concave points (standard error): 0.0 0.053
symmetry (standard error): 0.008 0.079
fractal dimension (standard error): 0.001 0.03
radius (worst): 7.93 36.04
texture (worst): 12.02 49.54
perimeter (worst): 50.41 251.2
area (worst): 185.2 4254.0
smoothness (worst): 0.071 0.223
compactness (worst): 0.027 1.058
concavity (worst): 0.0 1.252
concave points (worst): 0.0 0.291
symmetry (worst): 0.156 0.664
fractal dimension (worst): 0.055 0.208
===================================== ====== ======

:Missing Attribute Values: None

:Class Distribution: 212 - Malignant, 357 - Benign

:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian

:Donor: Nick Street

:Date: November, 1995

This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2

Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass. They describe
characteristics of the cell nuclei present in the image.

Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree. Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.

The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

.. dropdown:: References

- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
San Jose, CA, 1993.
- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
prognosis via linear programming. Operations Research, 43(4), pages 570-577,
July-August 1995.
- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
163-171.

#读取乳腺癌数据
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
X = data.data
y = data.target
#显示数据集
print(data.DESCR)
#显示数据dataframe结果
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target
df.head()
#写入csv文件
df.to_csv('breast_cancer.csv', index=False)

聚合系数法确定最优聚类数。

#使用聚合系数确定最佳聚类数
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_score
import matplotlib.pyplot as plt
sil = []
for i in range(2,11):
    model = AgglomerativeClustering(n_clusters=i)
    y_pred = model.fit_predict(X)
    sil.append(silhouette_score(X, y_pred))
plt.plot(range(2,11), sil)
plt.show()

谱系图绘制


#根据最佳聚类数绘制树状图
from scipy.cluster.hierarchy import dendrogram, ward
model = AgglomerativeClustering(n_clusters=2)
    
y_pred = model.fit_predict(X)
linkage_array = ward(X)
dendrogram(linkage_array)
ax = plt.gca()
ax.set_xlabel("Sample index")
ax.set_ylabel("Cluster distance")
bounds = ax.get_ybound()
ax.plot(bounds, [40, 40], '--', c='k')
ax.plot(bounds, [5, 5], '--', c='k')
plt.show()