page-blocks.arff

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UCI/page-blocks.arff
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2009-10-30 13:11:47
Description

Blocks Classification
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% 1. Title of Database: Blocks Classification
% 2. Sources:
%    (a) Donato Malerba
%        Dipartimento di Informatica
%        University of Bari
%        via Orabona 4
%        70126 Bari - Italy
%        phone: +39 - 80 - 5443269
%        fax: +39 - 80 - 5443196
%        malerbad@vm.csata.it
%    (b) Donor: Donato Malerba
%    (c) Date: July 1995
% 3. Past Usage:
%    This data set have been used to try different simplification methods
%    for decision trees. A summary of the results can be found in:
% 
%    Malerba, D., Esposito, F., and Semeraro, G.
%    "A Further Comparison of Simplification Methods for Decision-Tree Induction."
%    In D. Fisher and H. Lenz (Eds.), "Learning  from Data: 
%    Artificial Intelligence and Statistics V", Lecture Notes in Statistics,
%    Springer Verlag, Berlin, 1995.
% 
%    The problem consists in classifying all the blocks of the page
%    layout of a document that has been detected by a segmentation
%    process. This is an essential step in document analysis
%    in order to separate text from graphic areas. Indeed, 
%    the five classes are: text (1), horizontal line (2),
%    picture (3), vertical line (4) and graphic (5).
%    For a detailed presentation of the problem see:
% 
%     Esposito F., Malerba D., & Semeraro G.
%   Multistrategy Learning for Document Recognition
%          Applied Artificial Intelligence, 8, pp. 33-84, 1994
% 
%    All instances have been personally checked so that
%    low noise is present in the data.
% 
% 4. Relevant Information Paragraph:
% 
%    The 5473 examples comes from 54 distinct documents. 
%    Each observation concerns one block. 
%    All attributes are numeric.
%    Data are in a format readable by C4.5.
% 
% 5. Number of Instances: 5473.
% 
% 6. Number of Attributes 
% 
%    height:   integer.         | Height of the block.
%    lenght:   integer.     | Length of the block. 
%    area:     integer.    | Area of the block (height * lenght);
%    eccen:    continuous.  | Eccentricity of the block (lenght / height);
%    p_black:  continuous.  | Percentage of black pixels within the block (blackpix / area);
%    p_and:    continuous.        | Percentage of black pixels after the application of the Run Length Smoothing Algorithm (RLSA) (blackand / area);
%    mean_tr:  continuous.      | Mean number of white-black transitions (blackpix / wb_trans);
%    blackpix: integer.    | Total number of black pixels in the original bitmap of the block.
%    blackand: integer.        | Total number of black pixels in the bitmap of the block after the RLSA.
%    wb_trans: integer.          | Number of white-black transitions in the original bitmap of the block.
% 
% 
% 
% 7. Missing Attribute Values:  No missing value.
% 
% 8. Class Distribution: 
% 
%                                            Valid    Cum
%    Class               Frequency  Percent  Percent  Percent
%  
% text                      4913     89.8     89.8     89.8
% horiz. line                329      6.0      6.0     95.8
% graphic                     28       .5       .5     96.3
% vert. line                  88      1.6      1.6     97.9
% picture                    115      2.1      2.1    100.0
%                                 -------  -------  -------
%                         TOTAL      5473    100.0    100.0
% 
% Summary Statistics:
% 
% Variable      Mean    Std Dev   Minimum   Maximum   Correlation 
% 
% HEIGHT       10.47      18.96         1       804         .3510
% LENGTH       89.57     114.72         1       553        -.0045
% AREA       1198.41    4849.38         7    143993         .2343
% ECCEN        13.75      30.70      .007    537.00         .0992
% P_BLACK        .37        .18      .052      1.00         .2130
% P_AND          .79        .17      .062      1.00        -.1771
% MEAN_TR       6.22      69.08      1.00   4955.00         .0723
% BLACKPIX    365.93    1270.33         7     33017         .1656
% BLACKAND    741.11    1881.50         7     46133         .1565
% WB_TRANS    106.66     167.31         1      3212         .0337
% 
%
% Information about the dataset
% CLASSTYPE: nominal
% CLASSINDEX: last
%

@relation page-blocks

@attribute height INTEGER
@attribute lenght INTEGER
@attribute area INTEGER
@attribute eccen REAL
@attribute p_black REAL
@attribute p_and REAL
@attribute mean_tr REAL
@attribute blackpix INTEGER
@attribute blackand INTEGER
@attribute wb_trans INTEGER
@attribute class {1,2,3,4,5}

@data
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5,8,40,1.600,.550,1.00,2.44,22,40,9,1
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9,50,450,5.556,.496,.958,2.69,223,431,83,1
4,194,776,48.500,.263,.273,34.00,204,212,6,2
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2,22,44,11.000,.705,.864,15.50,31,38,2,2
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1,45,45,45.000,.756,1.00,5.67,34,45,6,2
1,75,75,75.000,.813,1.00,5.55,61,75,11,2
8,43,344,5.375,.349,.814,2.26,120,280,53,1
11,301,3311,27.364,.308,.735,2.22,1019,2435,458,1
7,35,245,5.000,.453,.873,2.47,111,214,45,1
9,122,1098,13.556,.339,.947,2.57,372,1040,145,1
10,130,1300,13.000,.425,.773,2.79,552,1005,198,1
6,46,276,7.667,.217,.804,2.00,60,222,30,1
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5,8,40,1.600,.350,.675,4.67,14,27,3,1
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5,35,175,7.000,.200,.829,4.38,35,145,8,1
4,47,188,11.750,.298,.739,2.33,56,139,24,1
3,31,93,10.333,.226,.774,2.10,21,72,10,1
3,16,48,5.333,.375,.938,2.25,18,45,8,1
7,122,854,17.429,.281,.626,1.73,240,535,139,1
11,21,231,1.909,.411,.680,3.96,95,157,24,1
11,8,88,.727,.375,.602,4.13,33,53,8,1
11,9,99,.818,.465,.818,9.20,46,81,5,1
10,8,80,.800,.500,1.00,5.00,40,80,8,1
10,21,210,2.100,.324,.600,3.40,68,126,20,1
2,157,314,78.500,.506,.551,26.50,159,173,6,2
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