% Date: Tue, 15 Nov 88 15:44:08 EST
% From: stan <stan@csi2.UofO.EDU>
% To: aha@ICS.UCI.EDU
% 
% 1. Title: Final settlements in labor negotitions in Canadian industry
% 
% 2. Source Information
%    -- Creators: Collective Barganing Review, montly publication,
%       Labour Canada, Industrial Relations Information Service,
%         Ottawa, Ontario, K1A 0J2, Canada, (819) 997-3117
%         The data includes all collective agreements reached
%         in the business and personal services sector for locals
%         with at least 500 members (teachers, nurses, university
%         staff, police, etc) in Canada in 87 and first quarter of 88.   
%    -- Donor: Stan Matwin, Computer Science Dept, University of Ottawa,
%                 34 Somerset East, K1N 9B4, (stan@uotcsi2.bitnet)
%    -- Date: November 1988
%  
% 3. Past Usage:
%    -- testing concept learning software, in particular
%       an experimental method to learn two-tiered concept descriptions.
%       The data was used to learn the description of an acceptable
%       and unacceptable contract.
%       The unacceptable contracts were either obtained by interviewing
%       experts, or by inventing near misses.
%       Examples of use are described in:
%         Bergadano, F., Matwin, S., Michalski, R.,
%         Zhang, J., Measuring Quality of Concept Descriptions, 
%         Procs. of the 3rd European Working Sessions on Learning,
%         Glasgow, October 1988.
%         Bergadano, F., Matwin, S., Michalski, R., Zhang, J.,
%         Representing and Acquiring Imprecise and Context-dependent
%         Concepts in Knowledge-based Systems, Procs. of ISMIS'88,
%         North Holland, 1988.
% 4. Relevant Information:
%    -- data was used to test 2tier approach with learning
% from positive and negative examples
% 
% 5. Number of Instances: 57 
% 
% 6. Number of Attributes: 16 
% 
% 7. Attribute Information:
%    1.  dur: duration of agreement 
%        [1..7]
%    2   wage1.wage : wage increase in first year of contract 
%        [2.0 .. 7.0]
%    3   wage2.wage : wage increase in second year of contract
%        [2.0 .. 7.0]
%    4   wage3.wage : wage increase in third year of contract
%        [2.0 .. 7.0]
%    5   cola : cost of living allowance 
%        [none, tcf, tc]
%    6   hours.hrs : number of working hours during week
%        [35 .. 40]
%    7   pension : employer contributions to pension plan
%        [none, ret_allw, empl_contr]
%    8   stby_pay : standby pay
%        [2 .. 25]
%    9   shift_diff : shift differencial : supplement for work on II and III shift
%        [1 .. 25]
%   10   educ_allw.boolean : education allowance 
%        [true false]
%   11   holidays : number of statutory holidays 
%        [9 .. 15]
%   12   vacation : number of paid vacation days
%        [ba, avg, gnr]
%   13   lngtrm_disabil.boolean : 
%        employer's help during employee longterm disabil
%        ity [true , false]
%   14   dntl_ins : employers contribution towards the dental plan
%        [none, half, full]
%   15   bereavement.boolean : employer's financial contribution towards the 
%        covering the costs of bereavement
%        [true , false]
%   16   empl_hplan : employer's contribution towards the health plan
%        [none, half, full]
% 
% 8. Missing Attribute Values: None
% 
% 9. Class Distribution:
% 
% 10. Exceptions from format instructions: no commas between attribute values.
% 
% 
@relation labor
@attribute duration numeric
@attribute wage-increase-first-year numeric
@attribute wage-increase-second-year numeric
@attribute wage-increase-third-year numeric
@attribute cost-of-living-adjustment {'none','tcf','tc'}
@attribute working-hours numeric
@attribute pension {'none','ret_allw','empl_contr'}
@attribute standby-pay numeric
@attribute shift-differential numeric
@attribute education-allowance {'yes','no'}
@attribute statutory-holidays numeric
@attribute vacation {'below_average','average','generous'}
@attribute longterm-disability-assistance {'yes','no'}
@attribute contribution-to-dental-plan {'none','half','full'}
@attribute bereavement-assistance {'yes','no'}
@attribute contribution-to-health-plan {'none','half','full'}
@attribute class {'bad','good'}

@data
1,5,-2147483648,-2147483648,nan,40,nan,nan,2,nan,11,'average',nan,nan,'yes',nan,'good'
2,4,5,-2147483648,nan,35,'ret_allw',nan,-2147483648,'yes',11,'below_average',nan,'full',nan,'full','good'
-2147483648,-2147483648,-2147483648,-2147483648,nan,38,'empl_contr',nan,5,nan,11,'generous','yes','half','yes','half','good'
3,3,4,5,'tc',-2147483648,nan,nan,-2147483648,'yes',-2147483648,nan,nan,nan,'yes',nan,'good'
3,4,4,5,nan,40,nan,nan,-2147483648,nan,12,'average',nan,'half','yes','half','good'
2,2,2,-2147483648,nan,35,nan,nan,6,'yes',12,'average',nan,nan,nan,nan,'good'
3,4,5,5,'tc',-2147483648,'empl_contr',nan,-2147483648,nan,12,'generous','yes','none','yes','half','good'
3,6,4,2,nan,40,nan,nan,3,nan,12,'below_average',nan,nan,nan,nan,'good'
2,3,7,-2147483648,nan,38,nan,12.0,25,'yes',11,'below_average','yes','half','yes',nan,'good'
1,5,-2147483648,-2147483648,'none',40,'empl_contr',nan,4,nan,11,'generous','yes','full',nan,nan,'good'
3,3,4,4,'none',36,nan,nan,3,nan,13,'generous',nan,nan,'yes','full','good'
2,6,6,-2147483648,nan,38,nan,nan,4,nan,15,nan,nan,'full',nan,nan,'good'
2,3,4,-2147483648,'none',40,nan,nan,2,'no',10,'below_average','no','half',nan,'half','bad'
3,3,4,5,'tcf',37,nan,nan,4,nan,13,'generous',nan,'full','yes','full','good'
1,3,-2147483648,-2147483648,'none',36,nan,nan,10,'no',11,'generous',nan,nan,nan,nan,'good'
2,4,4,-2147483648,'none',37,'empl_contr',nan,-2147483648,nan,11,'average',nan,'full','yes',nan,'good'
1,2,-2147483648,-2147483648,nan,35,nan,nan,2,nan,12,'below_average',nan,nan,nan,nan,'good'
1,2,-2147483648,-2147483648,'tc',40,'ret_allw',2.0,3,'no',9,'below_average','yes','half',nan,'none','bad'
1,2,-2147483648,-2147483648,'none',38,'none',nan,-2147483648,'yes',11,'average','no','none','no','none','bad'
2,4,5,-2147483648,'tcf',35,nan,13.0,5,nan,15,'generous',nan,nan,nan,nan,'good'
2,4,4,-2147483648,nan,38,nan,nan,4,nan,12,'generous',nan,'full',nan,'full','good'
2,2,3,-2147483648,nan,40,'none',nan,-2147483648,nan,11,'below_average',nan,nan,nan,nan,'bad'
3,3,4,4,'tcf',27,nan,nan,-2147483648,nan,-2147483648,nan,nan,nan,nan,nan,'good'
2,4,4,-2147483648,nan,40,nan,nan,4,nan,10,'generous',nan,'half',nan,'full','good'
1,6,-2147483648,-2147483648,nan,38,nan,8.0,3,nan,9,'generous',nan,nan,nan,nan,'good'
3,2,2,2,'none',40,'none',nan,-2147483648,nan,10,'below_average',nan,'half','yes','full','bad'
2,4,4,-2147483648,'tcf',-2147483648,nan,nan,-2147483648,'yes',10,'below_average','yes','none',nan,'half','good'
2,3,3,-2147483648,'none',33,nan,nan,-2147483648,'yes',12,'generous',nan,nan,'yes','full','good'
2,5,4,-2147483648,'none',37,nan,nan,5,'no',11,'below_average','yes','full','yes','full','good'
3,2,2,-2147483648,nan,35,'none',nan,-2147483648,nan,10,'average',nan,nan,'yes','full','bad'
3,4,4,5,'none',40,nan,nan,-2147483648,'no',11,'average',nan,'half',nan,nan,'good'
3,3,2,2,'tc',40,'none',nan,5,'no',10,'below_average','yes','half','yes','full','bad'
2,2,2,-2147483648,nan,38,'empl_contr',nan,-2147483648,nan,10,'average',nan,nan,nan,nan,'bad'
2,4,5,-2147483648,'none',40,'none',nan,3,'no',10,'below_average','no','none',nan,'none','bad'
3,2,2,2,'tc',40,'none',2.0,1,'no',10,'below_average','no','half','yes','full','bad'
2,2,2,-2147483648,'none',40,'none',nan,-2147483648,'no',11,'average','yes','none','yes','full','bad'
1,2,-2147483648,-2147483648,'tc',40,'ret_allw',4.0,0,'no',11,'generous','no','none','no','none','bad'
1,2,-2147483648,-2147483648,'none',38,'empl_contr',2.0,3,'no',9,'below_average','yes','half',nan,'none','bad'
3,2,2,2,nan,37,'empl_contr',nan,-2147483648,nan,10,'average',nan,nan,'yes','none','bad'
2,4,4,-2147483648,'none',40,nan,nan,4,nan,12,'average','yes','full','yes','half','good'
1,4,-2147483648,-2147483648,'none',-2147483648,'none',nan,-2147483648,'yes',11,'average','no','none','no','none','bad'
2,2,3,-2147483648,'none',38,'empl_contr',nan,-2147483648,'yes',12,'generous','yes','none','yes','full','bad'
2,2,2,-2147483648,'tc',39,'empl_contr',nan,-2147483648,nan,12,'average',nan,nan,'yes',nan,'bad'
2,2,3,-2147483648,'tcf',40,'none',nan,-2147483648,nan,11,'below_average',nan,nan,'yes',nan,'bad'
2,4,4,-2147483648,'none',40,'none',nan,3,nan,10,'below_average','no','none',nan,'none','bad'
2,4,4,-2147483648,nan,40,nan,nan,2,'no',10,'below_average','no','half',nan,'half','bad'
2,4,4,-2147483648,'none',40,nan,nan,5,nan,11,'average',nan,'full','yes','full','good'
2,4,4,-2147483648,'tcf',38,nan,nan,-2147483648,nan,-2147483648,nan,'yes','half',nan,'half','good'
2,5,4,-2147483648,'none',38,nan,14.0,5,nan,11,'below_average','yes',nan,nan,'full','good'
2,5,4,-2147483648,'none',40,'ret_allw',nan,-2147483648,nan,11,'average','yes','full','yes','full','good'
2,7,5,-2147483648,nan,-2147483648,nan,nan,-2147483648,nan,11,nan,'yes','full',nan,nan,'good'
3,2,3,-2147483648,'tcf',-2147483648,'empl_contr',nan,-2147483648,'yes',-2147483648,nan,'yes','half','yes',nan,'good'
3,3,4,4,'tcf',35,nan,nan,-2147483648,nan,13,'generous',nan,nan,'yes','full','good'
3,4,3,-2147483648,'none',40,'empl_contr',nan,6,nan,11,'average','yes','full',nan,'full','good'
3,5,4,-2147483648,'none',38,'empl_contr',10.0,6,nan,11,'generous','yes',nan,nan,'full','good'
3,5,5,5,nan,40,nan,nan,-2147483648,nan,12,'average',nan,'half','yes','half','good'
3,6,6,4,nan,35,nan,nan,14,nan,9,'generous','yes','full','yes','full','good'