KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

KEEL-dataset - data set description

Breast Cancer Wisconsin (Diagnostic) data set

Description
Files and additional references

Description

This section describes main characteristics of the wdbc data set and its attributes:

General information

Breast Cancer Wisconsin (Diagnostic) data set
Type	Classification	Origin	Real world
Features	30	(Real / Integer / Nominal)	(30 / 0 / 0)
Instances	569	Classes	2
Missing values?			No

Attribute description

Attribute	Domain	Attribute	Domain	Attribute	Domain
Radius1	[6.981, 28.11]	Radius2	[0.112, 2.873]	Radius3	[7.93, 36.04]
Texture1	[9.71, 39.28]	Texture2	[0.36, 4.885]	Texture3	[12.02, 49.54]
Perimeter1	[43.79, 188.5]	Perimeter2	[0.757, 21.98]	Perimeter3	[50.41, 251.2]
Area1	[143.5, 2501.0]	Area2	[6.802, 542.2]	Area3	[185.2, 4254.0]
Smoothness1	[0.053, 0.163]	Smoothness2	[0.0020, 0.031]	Smoothness3	[0.071, 0.223]
Compactness1	[0.019, 0.345]	Compactness2	[0.0020, 0.135]	Compactness3	[0.027, 1.058]
Concavity1	[0.0, 0.427]	Concavity2	[0.0, 0.396]	Concavity3	[0.0, 1.252]
Concave_points1	[0.0, 0.201]	Concave_points2	[0.0, 0.053]	Concave_points3	[0.0, 0.291]
Symmetry1	[0.106, 0.304]	Symmetry2	[0.0080, 0.079]	Symmetry3	[0.156, 0.664]
Fractal_dimension1	[0.05, 0.097]	Fractal_dimension2	[0.0010, 0.03]	Fractal_dimension3	[0.055, 0.208]
Class	{M, B}

Additional information

This database contains 30 features 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.

The task is to determine if a found tumor is benign or malignant (M = malignant, B = benign).

The ten real-valued features that are computed for each of three different cell nucleus are the following:

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

Files and additional references

In this section you can download some files related to the wdbc data set:

The complete data set already formatted in KEEL format can be downloaded from here .
A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here .
A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from here .
The header file associated to this data set can be downloaded from here .
This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29.