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 (Original) data set

Description
Files and additional references

Description

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

General information

Breast Cancer Wisconsin (Original) data set
Type	Classification	Origin	Real world
Features	9	(Real / Integer / Nominal)	(0 / 9 / 0)
Classes	2	Missing values?	Yes
Total instances	699	Instances without missing values	683

Attribute description

Attribute	Domain
ClumpThickness	[1, 10]
CellSize	[1, 10]
CellShape	[1, 10]
MarginalAdhesion	[1, 10]
EpithelialSize	[1, 10]
BareNuclei	[1, 10]
BlandChromatin	[1, 10]
NormalNucleoli	[1, 10]
Mitoses	[1, 10]
Class	{2,4}

Additional information

This dataset contains cases from a study that was conducted at the University of Wisconsin Hospitals, Madison, about patients who had undergone surgery for breast cancer. The task is to determine if the detected tumor is benign (2) os malignant (4).

To asses the data to classification process, the first attribute of the original data set (the sample code number) has been removed in this version.

Files and additional references

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

The complete data set already formatted in KEEL formatcan 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+%28Original%29.