\HeaderA{BreastCancer}{Wisconsin Breast Cancer Database}{BreastCancer}
\keyword{datasets}{BreastCancer}
\begin{Description}\relax
The objective is to identify each of a number of benign or malignant
classes. Samples arrive periodically as
Dr. Wolberg reports his clinical cases.
The database therefore reflects this chronological grouping of the
data.  This grouping information appears immediately below, having been
removed from the data itself.  Each variable except for the first was
converted into 11 primitive numerical attributes with values ranging
from 0 through 10.  There are 16 missing attribute values. See cited
below for more details.
\end{Description}
\begin{Usage}
\begin{verbatim}data(BreastCancer)\end{verbatim}
\end{Usage}
\begin{Format}\relax
A data frame with 699 observations on 11 variables, one being a
character variable, 9 being ordered or nominal, and 1 target class.

\Tabular{cll}{ 
[,1] & Id & Sample code number\\{}
[,2] & Cl.thickness & Clump Thickness\\{}
[,3] & Cell.size & Uniformity of Cell Size\\{}
[,4] & Cell.shape & Uniformity of Cell Shape\\{}
[,5] & Marg.adhesion  & Marginal Adhesion\\{}
[,6] & Epith.c.size & Single Epithelial Cell Size\\{}
[,7] & Bare.nuclei & Bare Nuclei\\{}
[,8] & Bl.cromatin & Bland Chromatin\\{}
[,9] & Normal.nucleoli & Normal Nucleoli\\{}
[,10] & Mitoses & Mitoses\\{}
[,11] & Class & Class
}
\end{Format}
\begin{Source}\relax
\Itemize{
\item Creator: Dr. WIlliam H. Wolberg (physician); University of
Wisconsin Hospital ;Madison; Wisconsin; USA 
\item Donor: Olvi Mangasarian (mangasarian@cs.wisc.edu)
\item Received: David W. Aha (aha@cs.jhu.edu)
}
These data have been taken from the UCI Repository Of Machine Learning
Databases at
\Itemize{
\item \url{ftp://ftp.ics.uci.edu/pub/machine-learning-databases}
\item \url{http://www.ics.uci.edu/~mlearn/MLRepository.html}
}
and were converted to R format by Evgenia.Dimitriadou@ci.tuwien.ac.at.
\end{Source}
\begin{References}\relax
1. Wolberg,W.H., \& Mangasarian,O.L. (1990). Multisurface method of 
pattern separation for medical diagnosis applied to breast cytology. In
Proceedings of the National Academy of Sciences, 87,
9193-9196.\\
- Size of data set: only 369 instances (at that point in time)\\
- Collected classification results: 1 trial only\\
- Two pairs of parallel hyperplanes were found to be consistent with
50\% of the data\\
- Accuracy on remaining 50\% of dataset: 93.5\%\\
- Three pairs of parallel hyperplanes were found to be consistent with
67\% of data\\
- Accuracy on remaining 33\% of dataset: 95.9\%

2. Zhang,J. (1992). Selecting typical instances in instance-based
learning.  In Proceedings of the Ninth International Machine
Learning Conference (pp. 470-479).  Aberdeen, Scotland: Morgan
Kaufmann.\\
- Size of data set: only 369 instances (at that point in time)\\
- Applied 4 instance-based learning algorithms\\
- Collected classification results averaged over 10 trials\\
- Best accuracy result: \\
- 1-nearest neighbor: 93.7\%\\
- trained on 200 instances, tested on the other 169\\
- Also of interest:\\
- Using only typical instances: 92.2\% (storing only 23.1 instances)\\
- trained on 200 instances, tested on the other 169
\end{References}

