stree_datasets/data/tanveer/trains/trains.names

1. Title: INDUCE Trains Data set

2. Sources:
   - Donor: GMU, Center for AI, Software Librarian,
            Eric E. Bloedorn (bloedorn@aic.gmu.edu)
   - Original owners: Ryszard S. Michalski (michalski@aic.gmu.edu)
     and Robert Stepp
   - Date received: 1 June 1994
   - Date updated: 24 June 1994 (Thanks to Larry Holder (UT Arlington)
     for noticing a translation error)

3. Past usage:
   - This set most closely resembles the data sets described in the following
     two publications:
      1. R.S. Michalski and  J.B. Larson "Inductive Inference of VL
         Decision Rules" In Proceedings of the Workshop in Pattern-Directed 
         Inference Systems, Hawaii, May 1977.  Also published in SIGART
         Newsletter, ACM No. 63, pp. 38-44, June 1977.
      2. Stepp, R.E. and Michalski, R.S. "Conceptual Clustering: Inventing 
         Goal-Oriented Classifications of Structured Objects"  In 
         R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Eds.) "Machine
         Learning: An Artificial Intelligence Approach, Volume II".  Los
         Altos, Ca: Morgan Kaufmann.  
  
   Both of these papers describe a set of 10 trains, 5 east-bound and 5 west
   bound.  Both refer to the same 10 trains as seen by the figures in these
   publications.   The differences are:
     1) This dataset has 10 attributes, no wheel, or load color attributes
     2) Reference 2 (Stepp, Michalski): does not completely list the 
        attributes used, but does mention wheel color - an attribute not 
        present in this dataset.
     3) Reference 1 (Michalski, Larson): 12 attributes mentioned, but only 6
        are explicitly described. These 6 are included in the dataset below 
        and the Stepp and Michalski set.

   Results:
    [1] Michalski and Larson found the following decision rules:
      (1) There exists car1, car2, lod1 and lod2 such that
          [infront(car1, car2)][lcont(car1, lod1)][lcont(car2,lod2)]
          [load-shape(lod1)=triangle][load-shape(lod2)=polygon]=>[dir=east]
      (2) There exists a car1 such that
          [ln(car1)=short][car-shape(car1)=closed-top]=>[dir=east]
      (3) [ncar=3]v There exists car1 such that [car1(car-shape(car1)=jagged-
          top] =>[dir=west]
          There exists car1 such that 
      (4) [#cars(ln=long)=2][cshape(car1)=open,trapezoind,u-shaped] v
          [location(car1)=2][cshape(car1)=closed, rectangle]=>[dir=west]
       (The first selector in rule 4 uses a meta descriptor generated by
        the program that counts the number of long cars in a train)
    [2] The goal of the cluster research is to develop a general method
        for clustering structured objects that can generate conjunctive
        descriptions that occur in human classifications or invent new
        concepts that have similar appeal. CLUSTER/S was able to find the
        following cognitively appealing clusters: 1) a) "There are two 
        different car shapes in the train" b) "There are three or more
        different car shapes in the train" 2) a) Wheels on all cars have 
        the same color, b) wheels on all cars do not have the same color."

4. Relevant information:
   - Additional "background" knowledge is supplied that provides a partial
     ordering on some of the attribute values.
   - We are providing this dataset both in its original form and in a form
     similar to the more typical propositional datasets in our repository.
     Since the trains dataset records relations between attributes, this
     transformation was somewhat challenging.  However, it may shed some
     insight on this problem for people who are more familiar with the simple
     one-instance-per-line dataset format.
   - Hierarchy of values:
     if (cshape is one of {openrect,opentrap,ushaped,dblopnrect}
       then cshape is opentop
     if (cshape is one of {hexagon,ellipse,closedrect,jaggedtop,slopetop,
                           engine}
       then cshape closedtop
   - Prediction task: Determine concise decision rules distinguishing 
                      trains traveling east from those traveling west.

5. Number of instances: 10

6. Number of attributes:
   - 10, not including the class attribute
  1.   ccont(train idx1, car idx2): car idx is contained in train idx
  2.             ncar(train idx): # of trains in car train idx (int)
  3. infront(car idx1, car idx2): relative positions of cars in train
  4.                loc(car idx): absolute position of car in train (int)
  5.               nwhl(car idx): # of wheels of car idx (int)
  6.                 ln(car idx): length of car idx (long, short)
  7.             cshape(car idx): shape of car (engine, dblopenrect, 
                                  closedrect, openrect, opentrap, ushaped,
                                  hexagon, ellipse, jaggedtop, slopetop,
                                  opentop, closedtop) 
  8.                npl(car idx): number of loads in car idx
  9.    lcont(car idx, load idx): description of which cars hold which loads
 10.           lhshape(load idx): description of load shape (trianglod, 
                                  rectanglod, circlelod, hexagonlod)
 Class: direction (east, west)

 The following format was used for the "transformed" dataset representation
 as found in trains.transformed.data (one instance per line):

 Attributes: 33
  1. Number_of_cars (integer in [3-5])
  2. Number_of_different_loads (integer in [1-4])
  3-22: 5 attributes for each of cars 2 through 5: (20 attributes total)
    - num_wheels (integer in [2-3])
    - length (short or long)    
    - shape (closedrect, dblopnrect, ellipse, engine, hexagon,
             jaggedtop, openrect, opentrap, slopetop, ushaped)
    - num_loads (integer in [0-3])
    - load_shape (circlelod, hexagonlod, rectanglod, trianglod)
  23-32: 10 Boolean attributes describing whether 2 types of loads are on
         adjacent cars of the train
    - Rectangle_next_to_rectangle (0 if false, 1 if true)
    - Rectangle_next_to_triangle (0 if false, 1 if true)
    - Rectangle_next_to_hexagon (0 if false, 1 if true)
    - Rectangle_next_to_circle (0 if false, 1 if true)
    - Triangle_next_to_triangle (0 if false, 1 if true)
    - Triangle_next_to_hexagon (0 if false, 1 if true)
    - Triangle_next_to_circle (0 if false, 1 if true)
    - Hexagon_next_to_hexagon (0 if false, 1 if true)
    - Hexagon_next_to_circle (0 if false, 1 if true)
    - Circle_next_to_circle (0 if false, 1 if true)
  33. Class attribute (east or west)
  
  The number of cars vary between 3 and 5.  Therefore, attributes referring
  to properties of cars that do not exist (such as the 5 attriubutes for
  the "5th" car when the train has fewer than 5 cars) are assigned a value
  of "-".

7. Distribution of classes:
   - There are 5 east-bound trains and 5 west-bound trains 
     (i.e., 50% east, 50% west)