% %%%%%%%%%%%%%%%%%%
% Data-Description %
% %%%%%%%%%%%%%%%%%%
% 
%                           COIL 1999 Competition Data
% 
% Data Type
% 
%    multivariate
% 
% Abstract
% 
%    This data set is from the 1999 Computational Intelligence and Learning
%    (COIL) competition. The data contains measurements of river chemical
%    concentrations and algae densities.
% 
% Sources
% 
%     Original Owner
% 
% [1]ERUDIT
% European Network for Fuzzy Logic and Uncertainty Modelling
% in Information Technology
% 
%     Donor
% 
% Jens Strackeljan
% Technical University Clausthal
% Institute of Applied Mechanics
% Graupenstr. 3, 38678 Clausthal-Zellerfeld, Germany
% [2]tmjs@itm.tu-clausthal.de
% 
%    Date Donated: September 9, 1999
% 
% Data Characteristics
% 
%    This data comes from a water quality study where samples were taken
%    from sites on different European rivers of a period of approximately
%    one year. These samples were analyzed for various chemical substances
%    including: nitrogen in the form of nitrates, nitrites and ammonia,
%    phosphate, pH, oxygen, chloride. In parallel, algae samples were
%    collected to determine the algae population distributions.
% 
% Other Relevant Information
% 
%    The competition involved the prediction of algal frequency
%    distributions on the basis of the measured concentrations of the
%    chemical substances and the global information concerning the season
%    when the sample was taken, the river size and its flow velocity. The
%    competition [3]instructions contain additional information on the
%    prediction task.
% 
% Data Format
% 
%    There are a total of 340 examples each containing 17 values. The first
%    11 values of each data set are the season, the river size, the fluid
%    velocity and 8 chemical concentrations which should be relevant for
%    the algae population distribution. The last 8 values of each example
%    are the distribution of different kinds of algae. These 8 kinds are
%    only a very small part of the whole community, but for the competition
%    we limited the number to 7. The value 0.0 means that the frequency is
%    very low. The data set also contains some empty fields which are
%    labeled with the string XXXXX.
% 
%    The training data are saved in the file: analysis.data (ASCII format).
% 
%    Table 1: Structure of the file analysis.data
% 
%    A
% 
% 
%    K
% 
%    a
% 
% 
%    g
% 
%    CC[1,1]
% 
% 
%    CC[1,11]
% 
%    AG[1,1]
% 
% 
%    AG[1,7]
% 
%    CC[200,1]
% 
% 
%    CC[200,11]
% 
%    AG[200,1]
% 
% 
%    AG[200,7]
% 
%    Explanation:
%    CC[i,j]: Chemical concentration or river characteristic
%    AG[i,j]: Algal frequency
% 
%    The chemical parameters are labeled as A, ..., K. The columns of the
%    algaes are labeled as a, ..,g.
% 
% Past Usage
% 
%    [4]The Third (1999) International COIL Competition Home Page
%      _________________________________________________________________
% 
% 
%     [5]The UCI KDD Archive
%     [6]Information and Computer Science
%     [7]University of California, Irvine
%     Irvine, CA 92697-3425
% 
%    Last modified: October 13, 1999
% 
% References
% 
%    1. http://www.erudit.de/
%    2. mailto:tmjs@itm.tu-clausthal.de
%    3. file://localhost/research/ml/datasets/uci/raw/data/ucikdd/coil/instructions.txt
%    4. http://www.erudit.de/erudit/activities/ic-99/index.htm
%    5. http://kdd.ics.uci.edu/
%    6. http://www.ics.uci.edu/
%    7. http://www.uci.edu/
% 
% %%%%%%%%%%%%%%%%%%
% Task-Description %
% %%%%%%%%%%%%%%%%%%
% 
% 
% Third International Competition 
% 
% Protecting rivers and streams by monitoring chemical concentrations and 
% algae communities.
% 
% 
% Intelligent Techniques for Monitoring Water Quality using chemical
% indicators and algae population
% 
% Recent years have been characterised by increasing concern at the
% impact man is having on the environment.  
% The impact on the environment of toxic waste, from a wide variety
% of manufacturing processes, is well known. More recently, however,
% it has become clear that the more subtle effects of nutrient level
% and chemical balance changes arising from farming land run-off and
% sewage water treatment also have a serious, but indirect, effect on
% the states of rivers, lakes and even the sea.  In temperate climates
% across the world summers are characterized by numerous reports excessive
% summer algae growth resulting in poor water clarity, mass deaths of
% river fish from reduced oxygen levels and the closure of recreational
% water facilities on account of the toxic effects of this annual algal bloom.
% Reducing the impact of these man-made changes in river nutrient levels
% has stimulated much biological research with the aim of identifying
% the crucial chemical control variables for the biological
% processes.  
% 
% The data used in this problem comes from one such study.
% During the research study water quality samples were
% taken from sites on different European rivers of a period of
% approximately one year.  These samples were analyzed for various
% chemical substances including: nitrogen in the form of nitrates,
% nitrites and ammonia, phosphate, pH, oxygen, chloride.
% In parallel, algae samples were collected to determine the algae population
% distributions. It is well known that the dynamics of the
% algae community is determined by external chemical 
% environment with one or more factors being predominant.
% While the chemical analysis is cheap and easily 
% automated, the biological part involves microscopic examination,
% requires trained manpower and is therefore both 
% expensive and slow. 
% 
% Diatoms like Cymbella are major contributors to primary production
% throughout the world. The diatom reacts with 
% large sensitivity to even small changes in acidity . 
% 
% Over a three and half billion year history algae have evolved and
% adapted as primary plant colonizers of almost 
% every known habitant in terrestrial and aquatic environments.
% They respond very rapidly to man-made environment changes.
% 
% 
% 
% The relationship between the chemical and biological features is
% complex and can be expected to need the application of advanced
% techniques. Typical of such real-life problems, the particular
% data set for the problem contains a mixture of (fuzzy) qualiative
% variables and numerical measurement values, with much of the data
% being incomplete.
% 
% The competition task is the prediction of algal frequency distributions
% on the basis of  the measured concentrations of the chemical
% substances and the global information concerning the season when the sample
% was taken, the river size and its flow velocity. The two last variables
% are given as linguistic variables.
% 
% 340 data sets were taken and each contain 17 values. The
% first 11 values of each data set are the season, the river
% size, the fluid velocity and 8 chemical concentrations which
% should be relevant for the algae population distribution.
% The last 8 values of each data set are the distribution of
% different kinds of algae. These 8 kinds are only a very small
% part of the whole community, but for the competition we limited
% the number to 7. The value 0.0 means that the frequency is very low.
% The data set also contains some empty fields which are labeled
% with the string XXXXX.
% 
% Each participant in the competition receives 200 complete data sets
% (training data) and 140 data sets (evaluation data) containing only
% the 11 values of the river descriptions and the chemical concentrations.
% 
% This training data is to be used in obtainin
% a 'model' providing a prediction of the algal distributions associated
% with the evaluation data.
% 
% 
% 
% The training data are saved in the file:
% 
% analysis.txt (ASCII format).
% 
% Structure of the file analysis.txt
% 
% A                          K              a                   g
% CC1,1   ...                CC1,11         AG1,1    ...        AG1,7
% ....                        ...            ...                 ...
% 
% 
% CC200,1 ...                CC200,11       AG240,1  ...        AG240,7
% 
% 
% Explanation:
% CCi,j:  Chemical concentration    j=1,..11
% AGi,k: Algal frequency            k=1...7
% 
% 
% The chemical parameters are labeled as A, ..., K.
% The columns of the algaes are labeled as a, ..,g.
% 
% 
% Evaluation data are saved in file eval.txt (ASCII format).
% 
% 
% Table 2: Structure of the file eval.* 
% A                               K
% CC1,1     ...                   CC1,11
% 
% .....                            ...
%                                 
% CC140,1   ...                   CC140,11
% 
% _____________________________________________________________
% 
% Objective 
% 
% The objective of the competition is to provide a prediction
% model on basis of the training data. Having obtained this
% prediction model, each participant must provide the solution
% in the form of the results of applying this model to the
% evaluation data. The results obtained in this way should
% correspond to the results of the evaluation data
% (which are known to the organizer). The criteria used to evaluate
% the results is given below.
% All 7 Algae frequency distributions must be determined.
% For this purpose any number of partial models may be developed.
% 
% _____________________________________________________________
% 
% Judgment of the results
% 
% To judge the results, the sum of squared errors will be calculated.
% The following Table describes the results of a particular participant.
% 
% Matrix of results
% a                     g
% 
% Res1,1   ...          Res1,7
% 
% ....                   ...
% 
% Res140,1              Res140,7
% 
% 
% All solutions that lead to a smallest total error will
% be regarded as winner of the contest. 
% 
% 
% 
% Information about the dataset
% CLASSTYPE: numeric
% CLASSINDEX: last
% 
% ALGAE #: 2/7
@relation coil-test-2
@attribute season {autumn,spring,summer,winter}
@attribute river_size {large_,medium,small_}
@attribute fluid_velocity {high__,low___,medium}
@attribute concentration_1 numeric
@attribute concentration_2 numeric
@attribute concentration_3 numeric
@attribute concentration_4 numeric
@attribute concentration_5 numeric
@attribute concentration_6 numeric
@attribute concentration_7 numeric
@attribute concentration_8 numeric
@attribute algae_2 numeric

@data
summer,small_,medium,7.95,5.7,57.333,2.46,273.33301,295.66699,380.0,nan,36.5
winter,small_,medium,7.98,8.8,59.333,7.392,286.66699,33.333,138.0,7.1,0.0
summer,small_,medium,8.0,7.2,80.0,1.957,174.286,47.857,113.714,4.5,23.0
spring,small_,high__,8.35,8.4,68.0,3.026,458.0,45.2,111.8,3.2,38.2
spring,small_,medium,8.1,13.2,19.0,0.0,130.0,6.0,40.0,2.0,55.4
summer,small_,medium,8.37,12.1,12.85,0.84,15.0,5.0,10.507,13.8,2.4
spring,small_,high__,7.31,9.9,6.0,1.395,58.75,6.0,16.0,0.8,1.7
autumn,small_,high__,7.91,11.2,5.0,1.383,6.0,24.333,30.0,32.0,2.0
summer,small_,high__,7.99,10.7,4.0,1.368,117.0,17.25,44.75,0.8,1.7
autumn,small_,high__,7.82,11.5,8.18,1.488,39.0,16.0,139.5,0.4,0.0
summer,small_,high__,6.6,10.8,4.0,1.18,80.0,2.0,59.0,0.6,0.0
autumn,small_,high__,6.79,9.4,11.42,1.966,42.0,3.0,15.0,0.6,0.0
summer,small_,high__,6.78,10.2,10.704,1.46,46.0,3.0,13.714,0.7,0.0
summer,small_,high__,7.8,10.8,14.568,1.228,61.25,34.5,62.0,1.1,0.0
spring,small_,high__,8.3,12.7,27.0,4.04,10.0,363.0,482.0,6.0,2.7
autumn,small_,high__,8.2,11.3,6.0,1.56,10.0,2.0,5.0,nan,0.0
summer,small_,high__,8.2,10.4,3.577,0.788,10.583,1.667,2.088,0.8,0.0
autumn,small_,medium,8.1,6.4,21.2,3.222,44.0,54.8,155.0,61.52,14.7
summer,small_,medium,8.54,12.83,22.545,4.0,170.5,68.0,116.069,41.6,5.0
autumn,small_,medium,8.5,7.8,71.0,11.02,500.0,121.0,nan,7.1,0.0
spring,small_,medium,7.7,6.8,65.0,1.833,782.5,77.25,340.0,9.0,5.1
autumn,small_,high__,8.4,10.5,50.6,10.494,334.0,209.10001,276.66699,20.72,3.0
summer,small_,high__,8.5,11.5,57.292,10.526,312.60001,261.39999,299.39999,23.5,1.5
summer,small_,high__,8.1,12.2,66.0,4.08,10.0,26.0,70.0,1.8,0.0
autumn,small_,low___,6.13,11.23,8.87,0.62,36.0,3.0,14.741,2.1,0.0
winter,small_,medium,nan,12.1,18.0,3.14,10.0,21.0,41.0,4.8,0.0
summer,small_,medium,7.2,10.4,18.0,2.42,80.0,11.0,44.0,2.5,1.1
spring,small_,medium,7.9,8.6,27.65,2.063,62.5,7.75,30.0,nan,0.0
autumn,small_,medium,7.8,9.1,36.124,5.974,169.0,13.091,71.057,3.3,0.0
summer,small_,high__,7.8,9.4,5.714,0.807,22.143,6.0,18.714,1.5,0.0
autumn,small_,high__,7.8,11.35,5.343,1.363,19.75,5.818,8.846,1.9,0.0
autumn,small_,high__,5.9,11.9,nan,1.88,5.0,1.0,2.0,nan,0.0
winter,small_,high__,6.8,9.1,nan,0.78,10.0,1.0,14.0,nan,0.0
summer,small_,medium,6.6,8.8,nan,0.95,20.0,1.0,7.0,nan,0.0
winter,small_,high__,6.6,11.8,nan,2.21,10.0,1.0,4.0,nan,0.0
winter,small_,medium,6.9,9.2,nan,2.21,10.0,2.0,13.0,nan,0.0
winter,small_,high__,7.66,10.8,4.0,0.997,15.0,1.5,7.333,1.0,0.0
autumn,small_,high__,7.6,10.5,3.05,1.002,13.333,1.667,10.833,nan,0.0
winter,medium,medium,8.0,7.0,37.091,2.237,146.364,84.091,172.778,2.3,19.4
spring,medium,medium,8.2,7.8,37.625,1.453,105.714,66.714,143.39999,2.6,5.1
autumn,medium,medium,8.2,10.7,134.66701,4.504,617.77802,49.444,164.778,19.2,23.4
summer,medium,medium,8.0,8.5,131.46899,3.454,792.0,63.1,286.60001,8.2,4.0
autumn,medium,high__,8.9,10.5,34.8,6.0,122.556,41.111,144.11099,27.03,1.0
summer,medium,high__,8.2,9.2,30.037,5.184,174.8,86.6,130.8,3.45,12.3
summer,medium,medium,7.8,8.8,29.078,2.823,263.556,27.0,95.12,11.5,6.9
summer,medium,high__,7.5,10.8,10.357,3.35,127.667,22.0,34.321,1.2,0.0
spring,medium,high__,7.4,9.0,13.75,5.268,58.75,56.25,64.0,2.5,0.0
spring,medium,medium,7.5,8.9,55.8,4.408,389.0,127.4,206.2,5.0,0.0
autumn,medium,medium,9.1,8.0,101.2,4.306,273.75,152.875,290.31299,10.7,1.0
autumn,medium,high__,8.9,8.0,60.2,4.033,306.47101,136.0,242.94099,18.4,7.7
summer,medium,low___,8.5,10.74,56.292,0.694,264.79999,43.4,124.942,30.48,4.1
autumn,medium,high__,8.3,8.6,75.0,5.18,560.0,30.5,170.0,16.7,1.5
spring,medium,high__,7.8,6.3,136.66701,3.734,154.444,35.556,175.33299,2.7,3.1
autumn,medium,medium,7.6,9.2,64.778,6.164,720.0,21.778,242.5,54.2,0.0
summer,medium,medium,7.5,9.2,61.557,7.035,558.33301,24.5,257.33301,19.5,0.0
autumn,medium,low___,7.5,8.6,57.5,7.368,577.0,67.3,254.444,22.0,0.0
spring,medium,low___,7.7,4.8,88.909,1.714,669.091,38.182,205.18201,2.8,1.5
spring,medium,low___,7.9,7.2,55.25,2.235,89.375,17.5,141.5,17.0,31.9
spring,medium,high__,8.06,2.2,39.0,2.085,773.125,90.75,163.25,26.0,12.1
autumn,medium,high__,8.5,7.5,9.3,1.557,260.0,9.6,18.1,3.9,1.2
autumn,medium,medium,8.2,10.4,63.3,0.389,217.14301,24.333,114.0,2.7,1.0
winter,medium,medium,8.0,4.8,58.767,0.308,93.75,33.375,110.875,2.7,15.1
autumn,medium,high__,8.7,10.8,1.118,0.534,26.364,14.818,20.9,1.4,0.0
spring,medium,high__,8.4,11.2,0.5,0.32,10.0,21.6,27.6,0.6,1.2
winter,medium,low___,8.5,8.3,36.583,5.632,440.83301,149.0,266.36401,19.827,3.2
autumn,medium,low___,8.3,8.8,64.768,6.272,357.16699,219.0,302.5,8.267,5.6
autumn,medium,medium,8.4,10.8,47.304,7.773,258.909,145.091,223.04401,13.36,8.6
autumn,medium,high__,7.9,11.9,11.862,2.209,128.636,48.091,69.079,2.755,0.0
autumn,medium,medium,9.13,12.0,30.496,4.971,99.6,64.6,146.265,54.13,25.2
autumn,medium,high__,7.4,11.4,12.031,1.621,176.8,36.3,58.599,36.1,0.0
summer,medium,medium,8.3,8.9,271.5,6.315,375.0,169.0,313.5,2.8,4.5
winter,medium,medium,8.2,10.4,41.0,5.16,410.0,38.0,61.0,6.0,15.9
summer,medium,medium,8.2,11.2,36.0,4.4,32.5,108.0,155.5,3.0,13.9
spring,medium,low___,8.17,6.3,37.3,0.527,82.0,62.0,133.10001,1.4,41.8
autumn,medium,low___,8.33,10.6,36.156,1.137,119.444,92.889,112.855,10.5,1.2
spring,medium,medium,8.5,6.7,45.609,4.411,160.0,88.364,180.364,32.833,22.9
autumn,medium,medium,8.1,9.1,47.267,9.367,169.091,75.0,127.778,3.667,1.0
winter,medium,high__,8.2,11.9,12.25,2.348,121.875,14.0,27.5,4.6,3.0
summer,medium,high__,8.1,9.4,11.0,2.251,48.75,17.375,66.875,2.5,0.0
summer,medium,low___,7.8,7.9,87.0,12.13,652.5,93.25,209.0,6.0,10.1
spring,medium,high__,8.26,5.0,44.818,0.526,97.273,105.455,181.636,20.6,10.3
summer,medium,high__,8.11,6.6,49.857,0.993,194.28,77.0,197.571,13.0,26.5
summer,medium,high__,7.87,1.8,49.25,0.611,357.125,128.25,185.125,4.5,8.8
winter,medium,high__,7.2,10.1,49.5,3.955,55.0,18.0,138.0,49.0,0.0
spring,medium,high__,7.8,8.3,51.5,2.098,30.2,24.6,184.39999,31.3,3.9
winter,medium,medium,7.9,11.3,82.5,6.283,300.0,12.333,53.333,13.7,0.0
summer,medium,medium,8.0,8.8,176.25,0.618,440.0,16.25,79.25,3.5,13.8
winter,large_,low___,7.7,9.3,66.0,3.56,310.0,37.0,nan,17.35,24.5
winter,large_,low___,8.7,5.4,48.0,1.139,144.286,36.714,66.833,22.017,12.0
summer,large_,low___,7.9,5.3,48.0,0.513,138.33299,61.333,89.167,4.0,9.8
autumn,large_,low___,8.7,12.2,32.23,1.887,233.5,17.5,66.167,39.333,7.0
winter,large_,low___,8.6,6.5,43.0,0.668,95.0,10.5,74.667,63.5,9.6
spring,large_,high__,7.4,7.3,19.0,4.39,120.0,74.857,166.286,5.3,0.0
summer,large_,high__,7.8,10.4,22.5,4.72,178.75,116.5,201.0,2.7,0.0
winter,large_,low___,8.5,9.8,70.25,1.644,285.0,68.714,132.0,16.028,4.6
spring,large_,low___,7.98,5.6,47.06,3.088,357.0,311.39999,342.29999,18.53,5.2
summer,large_,low___,7.95,7.2,57.286,3.746,425.71399,291.14301,330.0,4.714,6.2
spring,large_,medium,7.96,5.5,131.364,3.313,810.90002,311.45499,349.81799,20.47,8.3
winter,large_,medium,8.35,2.75,97.733,3.681,137.444,91.0,155.556,2.744,3.4
summer,large_,medium,8.15,10.4,189.567,5.011,162.944,135.778,219.278,2.859,19.2
winter,large_,high__,8.5,10.1,3.0,0.851,37.778,10.778,23.889,0.5,3.6
winter,large_,medium,8.5,11.4,3.0,0.774,10.909,3.727,8.091,3.6,12.4
spring,large_,medium,8.5,8.5,4.025,0.825,23.636,5.583,31.091,2.4,3.2
autumn,large_,medium,8.4,11.43,4.966,0.969,24.111,6.0,18.167,2.133,1.4
spring,large_,high__,8.2,9.9,6.4,0.553,21.429,12.0,76.286,1.3,2.1
autumn,large_,high__,8.0,10.98,9.7,0.874,67.7,26.6,51.034,2.2,0.0
autumn,large_,low___,8.3,8.9,42.058,5.922,116.727,150.58299,220.72301,6.7,45.4
spring,large_,low___,8.7,6.8,16.889,2.139,30.0,37.111,85.444,23.033,12.0
winter,large_,medium,8.6,10.4,15.182,2.502,140.909,31.909,77.7,15.318,0.0
summer,large_,medium,8.0,9.1,15.375,2.118,43.75,48.875,86.5,8.125,0.0
summer,large_,medium,8.2,9.5,17.875,2.363,63.75,44.0,77.0,8.463,29.5
spring,large_,medium,8.5,9.6,16.545,3.849,103.273,34.273,63.4,14.682,17.1
spring,large_,medium,8.04,9.3,130.263,3.776,131.008,97.5,152.966,6.15,8.7
autumn,large_,medium,7.95,9.1,76.886,3.461,93.827,68.333,146.049,3.95,31.2
summer,small_,high__,7.25,9.54,nan,0.642,85.0,14.6,19.45,0.46,0.0
autumn,small_,high__,7.64,10.3,34.235,2.942,41.43,17.0,41.567,7.43,0.0
winter,small_,high__,7.92,8.5,10.867,1.715,199.54,3.222,27.2,1.9,0.0
spring,small_,high__,7.62,9.4,11.055,1.51,13.56,4.0,12.65,1.456,8.6
summer,medium,high__,7.75,10.7,15.5,3.976,57.64,10.5,43.169,3.12,0.0
winter,small_,high__,7.08,8.4,9.45,1.572,26.54,4.0,13.6,0.675,0.0
summer,small_,high__,6.92,11.1,9.1,0.63,21.0,5.0,nan,2.46,1.9
winter,small_,high__,8.1,9.8,14.34,0.73,22.5,23.0,45.5,0.85,0.0
spring,small_,high__,7.2,11.3,8.97,0.23,134.5,13.0,19.0,nan,1.0
spring,large_,medium,8.61,10.1,3.518,0.663,12.22,3.222,7.0,1.3,2.0
summer,large_,medium,8.22,9.5,2.3,0.672,9.87,4.0,6.123,0.8,3.8
winter,large_,medium,8.53,10.5,3.0,0.758,10.35,4.1,nan,4.0,5.0
summer,large_,medium,8.4,10.0,3.51,0.866,29.65,5.8,15.0,2.86,6.7
winter,large_,high__,8.1,10.9,9.056,0.825,41.0,20.0,58.0,nan,19.6
summer,medium,high__,8.12,10.2,7.613,0.699,33.56,28.034,49.658,2.2,1.7
winter,large_,low___,8.43,10.8,35.642,6.225,134.0,103.5,nan,45.375,3.9
winter,large_,low___,8.7,11.7,21.4656,3.765,91.45,38.0,83.0,17.0,4.7
summer,large_,low___,8.1,8.2,26.54,2.805,42.75,48.5,88.125,13.98,12.0
autumn,large_,low___,8.35,11.1,22.56,3.14,76.2,41.0,98.665,17.456,7.0