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data/tanveer/breast-cancer-wisc/breast-cancer-wisconsin.names

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+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%)