| Converted to machine-readable form by Brian Frasca (6/14/94)
| Citation Request:
|    This breast cancer domain was obtained from the University Medical Centre,
|    Institute of Oncology, Ljubljana, Yugoslavia.  Thanks go to M. Zwitter and 
|    M. Soklic for providing the data.  Please include this citation if you plan
|    to use this database.
| 
| 1. Title: Breast cancer data (Michalski has used this)
| 
| 2. Sources: 
|    -- Matjaz Zwitter & Milan Soklic (physicians)
|       Institute of Oncology 
|       University Medical Center
|       Ljubljana, Yugoslavia
|    -- Donors: Ming Tan and Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
|    -- Date: 11 July 1988
| 
| 3. Past Usage: (Several: here are some)
|      -- Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The 
|         Multi-Purpose Incremental Learning System AQ15 and its Testing 
|         Application to Three Medical Domains.  In Proceedings of the 
|         Fifth National Conference on Artificial Intelligence, 1041-1045,
|         Philadelphia, PA: Morgan Kaufmann.
|         -- accuracy range: 66%-72%
|      -- Clark,P. & Niblett,T. (1987). Induction in Noisy Domains.  In 
|         Progress in Machine Learning (from the Proceedings of the 2nd
|         European Working Session on Learning), 11-30, Bled, 
|         Yugoslavia: Sigma Press.
|         -- 8 test results given: 65%-72% accuracy range
|      -- Tan, M., & Eshelman, L. (1988). Using weighted networks to 
|         represent classification knowledge in noisy domains.  Proceedings 
|         of the Fifth International Conference on Machine Learning, 121-134,
|         Ann Arbor, MI.
|         -- 4 systems tested: accuracy range was 68%-73.5%
|     -- Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A
|        Knowledge-Elicitation Tool for Sophisticated Users.  In I.Bratko
|        & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.
|        -- Assistant-86: 78% accuracy
| 
| 4. Relevant Information:
|      This is one of three domains provided by the Oncology Institute
|      that has repeatedly appeared in the machine learning literature.
|      (See also lymphography and primary-tumor.)
| 
|      This data set includes 201 instances of one class and 85 instances of
|      another class.  The instances are described by 9 attributes, some of
|      which are linear and some are nominal.
| 
| 5. Number of Instances: 286
| 
| 6. Number of Attributes: 9 + the class attribute
| 
| 7. Attribute Information:
|    1. Class: no-recurrence-events, recurrence-events
|    2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
|    3. menopause: lt40, ge40, premeno.
|    4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
|                   45-49, 50-54, 55-59.
|    5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26,
|                  27-29, 30-32, 33-35, 36-39.
|    6. node-caps: yes, no.
|    7. deg-malig: 1, 2, 3.
|    8. breast: left, right.
|    9. breast-quad: left-up, left-low, right-up,	right-low, central.
|   10. irradiat:	yes, no.
| 
| 8. Missing Attribute Values: (denoted by "?")
|    Attribute #:  Number of instances with missing values:
|    6.             8
|    9.             1.
| 
| 9. Class Distribution:
|     1. no-recurrence-events: 201 instances
|     2. recurrence-events: 85 instances


no-recurrence-events, recurrence-events.

age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
menopause: lt40, ge40, premeno.
tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
            45-49, 50-54, 55-59.
inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26,
           27-29, 30-32, 33-35, 36-39.
node-caps: yes, no.
deg-malig: 1, 2, 3.
breast: left, right.
breast-quad: left_up, left_low, right_up, right_low, central.
irradiat: yes, no.