| wdbc {mclust} | R Documentation |
The data set provides data for 569 patients on 30 features of the cell nuclei obtained from a digitized image of a fine needle aspirate (FNA) of a breast mass. For each patient the cancer was diagnosed as malignant or benign.
data(wdbc)
A data frame with 569 observations on the following variables:
IDID number
Diagnosiscancer diagnosis: M = malignant, B = benign
Radius_meana numeric vector
Texture_meana numeric vector
Perimeter_meana numeric vector
Area_meana numeric vector
Smoothness_meana numeric vector
Compactness_meana numeric vector
Concavity_meana numeric vector
Nconcave_meana numeric vector
Symmetry_meana numeric vector
Fractaldim_meana numeric vector
Radius_sea numeric vector
Texture_sea numeric vector
Perimeter_sea numeric vector
Area_sea numeric vector
Smoothness_sea numeric vector
Compactness_sea numeric vector
Concavity_sea numeric vector
Nconcave_sea numeric vector
Symmetry_sea numeric vector
Fractaldim_sea numeric vector
Radius_extremea numeric vector
Texture_extremea numeric vector
Perimeter_extremea numeric vector
Area_extremea numeric vector
Smoothness_extremea numeric vector
Compactness_extremea numeric vector
Concavity_extremea numeric vector
Nconcave_extremea numeric vector
Symmetry_extremea numeric vector
Fractaldim_extremea numeric vector
The recorded features are:
Radius as mean of distances from center to points on the perimeter
Texture as standard deviation of gray-scale values
Perimeter as cell nucleus perimeter
Area as cell nucleus area
Smoothness as local variation in radius lengths
Compactness as cell nucleus compactness, perimeter^2 / area - 1
Concavity as severity of concave portions of the contour
Nconcave as number of concave portions of the contour
Symmetry as cell nucleus shape
Fractaldim as fractal dimension, "coastline approximation" - 1
For each feature the recorded values are computed from each image as <feature_name>_mean, <feature_name>_se, and <feature_name>_extreme, for the mean, the standard error, and the mean of the three largest values.
UCI http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
Mangasarian, O. L., Street, W. N., and Wolberg, W. H. (1995) Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pp. 570-577.