wdbc: Wisconsin Diagnostic Breast Cancer (WDBC)
In kdevine: Multivariate Kernel Density Estimation with Vine Copulas
wdbc R Documentation
Wisconsin Diagnostic Breast Cancer (WDBC)
Description
The data contain measurements on cells in suspicious lumps in a women's breast.
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. All samples are classsified as either benign or malignant.
Usage
data(wdbc)
Format
wdbc is a data.frame with 31 columns. The first column indicates wether the sample is classified as benign (B) or malignant (M). The remaining columns contain measurements for 30 features.
Details
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 references listed below contain detailed descriptions of
how these features are computed.
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.
Note
This breast cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.
Source
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
References
O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear
programming",
SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
William H. Wolberg and O.L. Mangasarian: "Multisurface method of
pattern separation for medical diagnosis applied to breast cytology",
Proceedings of the National Academy of Sciences, U.S.A., Volume 87,
December 1990, pp 9193-9196.
K. P. Bennett & O. L. Mangasarian: "Robust linear programming
discrimination of two linearly inseparable sets",
Optimization Methods
and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).
Examples
data(wdbc)
str(wdbc)
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| wdbc | R Documentation |
Wisconsin Diagnostic Breast Cancer (WDBC)
Description
The data contain measurements on cells in suspicious lumps in a women's breast. 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. All samples are classsified as either benign or malignant.
Usage
data(wdbc)Format
wdbc is a data.frame with 31 columns. The first column indicates wether the sample is classified as benign (B) or malignant (M). The remaining columns contain measurements for 30 features.
Details
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 references listed below contain detailed descriptions of how these features are computed.
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.
Note
This breast cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.
Source
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
References
O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear
programming",
SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
William H. Wolberg and O.L. Mangasarian: "Multisurface method of
pattern separation for medical diagnosis applied to breast cytology",
Proceedings of the National Academy of Sciences, U.S.A., Volume 87,
December 1990, pp 9193-9196.
K. P. Bennett & O. L. Mangasarian: "Robust linear programming
discrimination of two linearly inseparable sets",
Optimization Methods
and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).
Examples
data(wdbc)
str(wdbc)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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