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data/tanveer/trains/trains.names
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133
data/tanveer/trains/trains.names
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1. Title: INDUCE Trains Data set
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2. Sources:
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- Donor: GMU, Center for AI, Software Librarian,
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Eric E. Bloedorn (bloedorn@aic.gmu.edu)
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- Original owners: Ryszard S. Michalski (michalski@aic.gmu.edu)
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and Robert Stepp
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- Date received: 1 June 1994
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- Date updated: 24 June 1994 (Thanks to Larry Holder (UT Arlington)
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for noticing a translation error)
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3. Past usage:
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- This set most closely resembles the data sets described in the following
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two publications:
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1. R.S. Michalski and J.B. Larson "Inductive Inference of VL
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Decision Rules" In Proceedings of the Workshop in Pattern-Directed
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Inference Systems, Hawaii, May 1977. Also published in SIGART
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Newsletter, ACM No. 63, pp. 38-44, June 1977.
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2. Stepp, R.E. and Michalski, R.S. "Conceptual Clustering: Inventing
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Goal-Oriented Classifications of Structured Objects" In
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R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Eds.) "Machine
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Learning: An Artificial Intelligence Approach, Volume II". Los
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Altos, Ca: Morgan Kaufmann.
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Both of these papers describe a set of 10 trains, 5 east-bound and 5 west
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bound. Both refer to the same 10 trains as seen by the figures in these
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publications. The differences are:
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1) This dataset has 10 attributes, no wheel, or load color attributes
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2) Reference 2 (Stepp, Michalski): does not completely list the
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attributes used, but does mention wheel color - an attribute not
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present in this dataset.
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3) Reference 1 (Michalski, Larson): 12 attributes mentioned, but only 6
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are explicitly described. These 6 are included in the dataset below
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and the Stepp and Michalski set.
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Results:
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[1] Michalski and Larson found the following decision rules:
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(1) There exists car1, car2, lod1 and lod2 such that
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[infront(car1, car2)][lcont(car1, lod1)][lcont(car2,lod2)]
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[load-shape(lod1)=triangle][load-shape(lod2)=polygon]=>[dir=east]
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(2) There exists a car1 such that
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[ln(car1)=short][car-shape(car1)=closed-top]=>[dir=east]
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(3) [ncar=3]v There exists car1 such that [car1(car-shape(car1)=jagged-
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top] =>[dir=west]
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There exists car1 such that
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(4) [#cars(ln=long)=2][cshape(car1)=open,trapezoind,u-shaped] v
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[location(car1)=2][cshape(car1)=closed, rectangle]=>[dir=west]
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(The first selector in rule 4 uses a meta descriptor generated by
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the program that counts the number of long cars in a train)
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[2] The goal of the cluster research is to develop a general method
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for clustering structured objects that can generate conjunctive
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descriptions that occur in human classifications or invent new
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concepts that have similar appeal. CLUSTER/S was able to find the
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following cognitively appealing clusters: 1) a) "There are two
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different car shapes in the train" b) "There are three or more
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different car shapes in the train" 2) a) Wheels on all cars have
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the same color, b) wheels on all cars do not have the same color."
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4. Relevant information:
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- Additional "background" knowledge is supplied that provides a partial
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ordering on some of the attribute values.
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- We are providing this dataset both in its original form and in a form
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similar to the more typical propositional datasets in our repository.
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Since the trains dataset records relations between attributes, this
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transformation was somewhat challenging. However, it may shed some
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insight on this problem for people who are more familiar with the simple
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one-instance-per-line dataset format.
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- Hierarchy of values:
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if (cshape is one of {openrect,opentrap,ushaped,dblopnrect}
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then cshape is opentop
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if (cshape is one of {hexagon,ellipse,closedrect,jaggedtop,slopetop,
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engine}
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then cshape closedtop
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- Prediction task: Determine concise decision rules distinguishing
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trains traveling east from those traveling west.
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5. Number of instances: 10
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6. Number of attributes:
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- 10, not including the class attribute
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1. ccont(train idx1, car idx2): car idx is contained in train idx
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2. ncar(train idx): # of trains in car train idx (int)
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3. infront(car idx1, car idx2): relative positions of cars in train
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4. loc(car idx): absolute position of car in train (int)
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5. nwhl(car idx): # of wheels of car idx (int)
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6. ln(car idx): length of car idx (long, short)
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7. cshape(car idx): shape of car (engine, dblopenrect,
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closedrect, openrect, opentrap, ushaped,
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hexagon, ellipse, jaggedtop, slopetop,
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opentop, closedtop)
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8. npl(car idx): number of loads in car idx
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9. lcont(car idx, load idx): description of which cars hold which loads
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10. lhshape(load idx): description of load shape (trianglod,
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rectanglod, circlelod, hexagonlod)
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Class: direction (east, west)
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The following format was used for the "transformed" dataset representation
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as found in trains.transformed.data (one instance per line):
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Attributes: 33
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1. Number_of_cars (integer in [3-5])
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2. Number_of_different_loads (integer in [1-4])
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3-22: 5 attributes for each of cars 2 through 5: (20 attributes total)
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- num_wheels (integer in [2-3])
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- length (short or long)
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- shape (closedrect, dblopnrect, ellipse, engine, hexagon,
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jaggedtop, openrect, opentrap, slopetop, ushaped)
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- num_loads (integer in [0-3])
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- load_shape (circlelod, hexagonlod, rectanglod, trianglod)
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23-32: 10 Boolean attributes describing whether 2 types of loads are on
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adjacent cars of the train
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- Rectangle_next_to_rectangle (0 if false, 1 if true)
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- Rectangle_next_to_triangle (0 if false, 1 if true)
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- Rectangle_next_to_hexagon (0 if false, 1 if true)
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- Rectangle_next_to_circle (0 if false, 1 if true)
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- Triangle_next_to_triangle (0 if false, 1 if true)
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- Triangle_next_to_hexagon (0 if false, 1 if true)
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- Triangle_next_to_circle (0 if false, 1 if true)
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- Hexagon_next_to_hexagon (0 if false, 1 if true)
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- Hexagon_next_to_circle (0 if false, 1 if true)
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- Circle_next_to_circle (0 if false, 1 if true)
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33. Class attribute (east or west)
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The number of cars vary between 3 and 5. Therefore, attributes referring
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to properties of cars that do not exist (such as the 5 attriubutes for
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the "5th" car when the train has fewer than 5 cars) are assigned a value
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of "-".
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7. Distribution of classes:
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- There are 5 east-bound trains and 5 west-bound trains
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(i.e., 50% east, 50% west)
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