% This dataset is synthetic.  It was generated by David Coleman
% at RCA Laboratories in Princeton, N.J.  For convenience, we will
% refer to it as the POLLEN DATA.  The first three variables are the
% lengths of geometric features observed sampled pollen grains - in the
% x, y, and z dimensions: a "ridge" along x, a "nub" in the y
% direction, and a "crack" in along the z dimension.  The fourth
% variable is pollen grain weight, and the fifth is density.
% 
% There are 3848 observations, in random order (for people whose
% software packages cannot handle this much data, it is recommended
% that the data be sampled).  The dataset is broken up into eight 
% pieces, POLLEN1.DAT - POLLEN8.DAT, each with 481 observations.  
% We will call the variables:
% 
% 1. RIDGE
% 2. NUB
% 3. CRACK
% 4. WEIGHT
% 5. DENSITY
% 
% 6. OBSERVATION NUMBER (for convenience)
% 
% The data analyst is advised that there is more than one "feature" to
% these data.  Each feature can be observed through various graphical
% techniques, but analytic methods, as well, can help "crack" the
% dataset.
%
% Additional Info:
%
%    I no longer have the description handed out during the JSM, but can
% tell you how I generated the data, in minitab.
% 
% 1. Part A was generated: 5000 (I think) 5-variable, uncorrelated, i.i.d.
% Gaussian observations.
% 
% 2. To get part B, I duplicated part A, then reversed the sign on the
% observations for 3 of the 5 variables.
% 
% 3. Part B was appended to Part A.
% 
% 4. The order of the observations was randomized.
% 
% 5. While waiting for my tardy car-pool companion, I took a piece of
% graph paper, and figured out a dot-matrix representation of the word,
% "EUREKA."  I then added these observations to the "center" of the
% datatset.
% 
% 6. The data were scaled, by variable (something like 1,3,5,7,11).
% 
% 7. The data were rotated, then translated.
% 
% 8. A few points in space within the datacloud were chosen as ellipsoid
% centers, then for each center, all observations within a (scaled and
% rotated) radius were identified, and eliminated - to form ellipsoidal
% voids.
% 
% 9. The variables were given entirely ficticious names.
% 
% FYI, only the folks at Bell Labs, Murray Hill, found everything,
% including the voids.
% 
% Hope this is helpful!
% 
% References:
% 
% Becker, R.A., Denby, L., McGill, R., and Wilks,
% A. (1986). Datacryptanalysis: A Case Study.
% Proceedings of the Section on Statistical Graphics, 92-97.
% 
% Slomka, M. (1986). The Analysis of a Synthetic Data Set.
% Proceedings of the Section on Statistical Graphics, 113-116.
% 
%
%
% Information about the dataset
% CLASSTYPE: numeric
% CLASSINDEX: none specific
%

@relation pollen

@attribute RIDGE REAL
@attribute NUB REAL
@attribute CRACK REAL
@attribute WEIGHT REAL
@attribute DENSITY REAL
@attribute OBSERVATION_NUMBER INTEGER

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5.3812,-0.6501,3.4748,-10.0867,-1.2737,1944
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6.0561,0.5155,15.6692,-6.3722,-1.9492,1960
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0.6400,6.2651,20.9542,5.6109,0.1776,1962
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8.3386,3.2802,-3.4340,-12.0139,-3.9347,1968
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12.1429,-0.2296,4.3198,-16.9767,-4.5292,1991
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2.6724,-7.0395,-5.1527,-4.1490,-0.9552,2007
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11.4531,-1.1022,-5.6240,-15.9041,-4.5962,2009
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8.4476,7.3515,-13.4074,-21.0049,-0.1173,2019
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