*********** Cancer.nns is a NeuNet Pro Sample File ***********

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    ***************  More about the Cancer Data *******************


| Citation Request:
|    This breast cancer databases was obtained from the University of Wisconsin
|    Hospitals, Madison from Dr. William H. Wolberg.  If you publish results
|    when using this database, then please include this information in your
|    acknowledgements.  Also, please cite one or more of:
| 
|    1. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear 
|       programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
| 
|    2. 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.
| 
|    3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition 
|       via linear programming: Theory and application to medical diagnosis", 
|       in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying
|       Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30.
| 
|    4. 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).
| 
| 1. Title: Wisconsin Breast Cancer Database (January 8, 1991)
| 
| 2. Sources:
|    -- Dr. WIlliam H. Wolberg (physician)
|       University of Wisconsin Hospitals
|       Madison, Wisconsin
|       USA
|    -- Donor: Olvi Mangasarian (mangasarian@cs.wisc.edu)
|       Received by David W. Aha (aha@cs.jhu.edu)
|    -- Date: 15 July 1992
| 
| 3. Past Usage:
| 
|    Attributes 2 through 10 have been used to represent instances.
|    Each instance has one of 2 possible classes: benign or malignant.
| 
|    1. Wolberg,~W.~H., \& Mangasarian,~O.~L. (1990). Multisurface method of 
|       pattern separation for medical diagnosis applied to breast cytology. In
|       {\it Proceedings of the National Academy of Sciences}, {\it 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 {\it 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
| 
| 4. Relevant Information:
| 
|    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:
| 
|      Group 1: 367 instances (January 1989)
|      Group 2:  70 instances (October 1989)
|      Group 3:  31 instances (February 1990)
|      Group 4:  17 instances (April 1990)
|      Group 5:  48 instances (August 1990)
|      Group 6:  49 instances (Updated January 1991)
|      Group 7:  31 instances (June 1991)
|      Group 8:  86 instances (November 1991)
|      -----------------------------------------
|      Total:   699 points (as of the donated datbase on 15 July 1992)
| 
|    Note that the results summarized above in Past Usage refer to a dataset
|    of size 369, while Group 1 has only 367 instances.  This is because it
|    originally contained 369 instances; 2 were removed.  The following
|    statements summarizes changes to the original Group 1's set of data:
| 
|    #####  Group 1 : 367 points: 200B 167M (January 1989)
|    #####  Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805
|    #####  Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record
|    #####                  : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial
|    #####                  : Changed 0 to 1 in field 6 of sample 1219406
|    #####                  : Changed 0 to 1 in field 8 of following sample:
|    #####                  : 1182404,2,3,1,1,1,2,0,1,1,1
| 
| 5. Number of Instances: 699 (as of 15 July 1992)
| 
| 6. Number of Attributes: 10 plus the class attribute
| 
| 7. Attribute Information: (class attribute has been moved to last column)
| 
|    #  Attribute                     Domain
|    -- -----------------------------------------
|    1. Sample code number            id number
|    2. Clump Thickness               1 - 10
|    3. Uniformity of Cell Size       1 - 10
|    4. Uniformity of Cell Shape      1 - 10
|    5. Marginal Adhesion             1 - 10
|    6. Single Epithelial Cell Size   1 - 10
|    7. Bare Nuclei                   1 - 10
|    8. Bland Chromatin               1 - 10
|    9. Normal Nucleoli               1 - 10
|   10. Mitoses                       1 - 10
|   11. Class:                        (2 for benign, 4 for malignant)
| 
| 8. Missing attribute values: 16
| 
|    There are 16 instances in Groups 1 to 6 that contain a single missing 
|    (i.e., unavailable) attribute value, now denoted by "?".  
| 
| 9. Class distribution:
|  
|    Benign: 458 (65.5%)
|    Malignant: 241 (34.5%)

benign, malignant.

Sample code number:		continuous
Clump Thickness:		continuous
Uniformity of Cell Size:	continuous
Uniformity of Cell Shape:	continuous
Marginal Adhesion:		continuous
Single Epithelial Cell Size:	continuous
Bare Nuclei:			continuous
Bland Chromatin:		continuous
Normal Nucleoli:		continuous
Mitoses:			continuous