Breast Cancer Wisconsin (Diagnostic) Data Set

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.
n 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/

Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

Attribute Information:

1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three
largest values) of these features were computed for each image,
resulting in 30 features. For instance, field 3 is Mean Radius, field
13 is Radius SE, field 23 is Worst Radius.

All feature values are recoded with four significant digits.

Missing attribute values: none

Class distribution: 357 benign, 212 malignant

该数据集的特征由乳腺肿块细针穿刺（FNA）的数字化图像计算得出。这些特征描述了图像中存在的细胞核的特征。在三维空间中的描述，可参考：[K. P. Bennett 和 O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34]。此数据库亦可通过威斯康星大学计算机科学系FTP服务器获取：
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/
亦可在UCI机器学习仓库找到：https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
属性信息如下：
1) 身份编号
2) 诊断（M = 恶性，B = 良性）
3-32) 对每个细胞核计算十个实值特征：
a) 半径（从中心到边缘点的距离平均值）
b) 纹理（灰度值的标准差）
c) 周长
d) 面积
e) 光滑度（半径长度的局部变化）
f) 紧凑度（周长平方除以面积减1.0）
g) 凹陷度（轮廓凹陷部分的程度）
h) 凹陷点（轮廓凹陷部分的数目）
i) 对称性
j) 分形维度（海岸线近似法 - 1）
这些特征的均值、标准误差以及“最差”或最大值（三个最大值的均值）对每个图像进行了计算，从而得到30个特征。例如，第3字段是均值半径，第13字段是半径标准误差，第23字段是最差半径。
所有特征值均以四位有效数字重新编码。
缺失属性值：无
类别分布：良性357例，恶性212例。