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Uni Köln
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Mosler > Prof. Mosler > Datenportal
Datenportal des Lehrstuhls für Statistik und Ökonometrie
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Wine (1 vs 3) data
The data set (and description) can be downloaded here:
http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
Description:
1. Title of Database: Wine recognition data
Updated Sept 21, 1998 by C.Blake : Added attribute information
2. Sources:
(a) Forina, M. et al, PARVUS - An Extendible Package for Data
Exploration, Classification and Correlation. Institute of Pharmaceutical
and Food Analysis and Technologies, Via Brigata Salerno,
16147 Genoa, Italy.
(b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
(c) July 1991
3. Past Usage:
(1)
S. Aeberhard, D. Coomans and O. de Vel,
Comparison of Classifiers in High Dimensional Settings,
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Technometrics).
The data was used with many others for comparing various
classifiers. The classes are separable, though only RDA
has achieved 100% correct classification.
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
(All results using the leave-one-out technique)
In a classification context, this is a well posed problem
with "well behaved" class structures. A good data set
for first testing of a new classifier, but not very
challenging.
(2)
S. Aeberhard, D. Coomans and O. de Vel,
"THE CLASSIFICATION PERFORMANCE OF RDA"
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Journal of Chemometrics).
Here, the data was used to illustrate the superior performance of
the use of a new appreciation function with RDA.
4. Relevant Information:
-- These data are the results of a chemical analysis of
wines grown in the same region in Italy but derived from three
different cultivars.
The analysis determined the quantities of 13 constituents
found in each of the three types of wines.
-- I think that the initial data set had around 30 variables, but
for some reason I only have the 13 dimensional version.
I had a list of what the 30 or so variables were, but a.)
I lost it, and b.), I would not know which 13 variables
are included in the set.
-- The attributes are (dontated by Riccardo Leardi,
riclea@anchem.unige.it )
1) Alcohol
2) Malic acid
3) Ash
4) Alcalinity of ash
5) Magnesium
6) Total phenols
7) Flavanoids
8) Nonflavanoid phenols
9) Proanthocyanins
10)Color intensity
11)Hue
12)OD280/OD315 of diluted wines
13)Proline
5. Number of Instances
class 1 59
class 2 71
class 3 48
6. Number of Attributes
13
7. For Each Attribute:
All attributes are continuous
No statistics available, but suggest to standardise
variables for certain uses (e.g. for us with classifiers
which are NOT scale invariant)
NOTE: 1st attribute is class identifier (1-3)
8. Missing Attribute Values:
None
9. Class Distribution: number of instances per class
class 1 59
class 2 71
class 3 48
Descriptive statistics:
Dataset= wine_1vs3 : n= 107 , d= 13
Class1: n= 59
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0.2136 -0.0129 -0.0156 -0.3746 0.7732 0.0659 0.0762 0.0005 0.0586 0.2337 0.0043 0.0115 36.9195
[2,] -0.0129 0.4741 0.0041 0.1053 0.5734 -0.0195 -0.0524 -0.0043 -0.0229 -0.2197 -0.0337 0.0426 -56.8364
[3,] -0.0156 0.0041 0.0516 0.3178 0.9124 0.0004 -0.0064 0.0074 -0.0136 -0.0350 0.0063 -0.0066 -1.4858
[4,] -0.3746 0.1053 0.3178 6.4838 6.3716 -0.1925 -0.2906 0.0539 -0.1822 -0.6653 0.0276 -0.1070 -69.0615
[5,] 0.7732 0.5734 0.9124 6.3716 110.2279 1.0934 0.5147 0.1745 -0.2555 2.4013 -0.1362 0.4523 -344.0041
[6,] 0.0659 -0.0195 0.0004 -0.1925 1.0934 0.1149 0.1083 -0.0004 0.0522 0.2729 -0.0089 0.0064 22.1502
[7,] 0.0762 -0.0524 -0.0064 -0.2906 0.5147 0.1083 0.1580 -0.0025 0.0899 0.3651 0.0004 -0.0126 33.4995
[8,] 0.0005 -0.0043 0.0074 0.0539 0.1745 -0.0004 -0.0025 0.0049 -0.0042 -0.0132 0.0034 -0.0081 -0.2379
[9,] 0.0586 -0.0229 -0.0136 -0.1822 -0.2555 0.0522 0.0899 -0.0042 0.1698 0.2168 0.0050 0.0005 12.9784
[10,] 0.2337 -0.2197 -0.0350 -0.6653 2.4013 0.2729 0.3651 -0.0132 0.2168 1.5341 0.0041 -0.0827 161.5407
[11,] 0.0043 -0.0337 0.0063 0.0276 -0.1362 -0.0089 0.0004 0.0034 0.0050 0.0041 0.0136 -0.0129 9.1180
[12,] 0.0115 0.0426 -0.0066 -0.1070 0.4523 0.0064 -0.0126 -0.0081 0.0005 -0.0827 -0.0129 0.1275 -27.5401
[13,] 36.9195 -56.8364 -1.4858 -69.0615 -344.0041 22.1502 33.4995 -0.2379 12.9784 161.5407 9.1180 -27.5401 49071.4500
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 1.0000 -0.0405 -0.1486 -0.3184 0.1594 0.4207 0.4149 0.0157 0.3076 0.4083 0.0800 0.0698 0.3606
[2,] -0.0405 1.0000 0.0262 0.0600 0.0793 -0.0835 -0.1913 -0.0894 -0.0808 -0.2576 -0.4200 0.1732 -0.3726
[3,] -0.1486 0.0262 1.0000 0.5493 0.3825 0.0048 -0.0705 0.4659 -0.1455 -0.1242 0.2392 -0.0816 -0.0295
[4,] -0.3184 0.0600 0.5493 1.0000 0.2383 -0.2230 -0.2871 0.3023 -0.1736 -0.2110 0.0930 -0.1177 -0.1224
[5,] 0.1594 0.0793 0.3825 0.2383 1.0000 0.3072 0.1233 0.2372 -0.0590 0.1847 -0.1114 0.1207 -0.1479
[6,] 0.4207 -0.0835 0.0048 -0.2230 0.3072 1.0000 0.8038 -0.0170 0.3736 0.6501 -0.2243 0.0532 0.2950
[7,] 0.4149 -0.1913 -0.0705 -0.2871 0.1233 0.8038 1.0000 -0.0895 0.5486 0.7416 0.0079 -0.0885 0.3804
[8,] 0.0157 -0.0894 0.4659 0.3023 0.2372 -0.0170 -0.0895 1.0000 -0.1445 -0.1525 0.4118 -0.3235 -0.0153
[9,] 0.3076 -0.0808 -0.1455 -0.1736 -0.0590 0.3736 0.5486 -0.1445 1.0000 0.4247 0.1039 0.0031 0.1422
[10,] 0.4083 -0.2576 -0.1242 -0.2110 0.1847 0.6501 0.7416 -0.1525 0.4247 1.0000 0.0282 -0.1869 0.5888
[11,] 0.0800 -0.4200 0.2392 0.0930 -0.1114 -0.2243 0.0079 0.4118 0.1039 0.0282 1.0000 -0.3107 0.3534
[12,] 0.0698 0.1732 -0.0816 -0.1177 0.1207 0.0532 -0.0885 -0.3235 0.0031 -0.1869 -0.3107 1.0000 -0.3482
[13,] 0.3606 -0.3726 -0.0295 -0.1224 -0.1479 0.2950 0.3804 -0.0153 0.1422 0.5888 0.3534 -0.3482 1.0000
Median: 13.7453 2.0431 2.4774 17.0148 105.3519 2.8009 2.9508 0.2665 1.77 5.1854 1.0248 3.1632 1092.672
Mean: 13.7447 2.0107 2.4556 17.0373 106.339 2.8402 2.9824 0.29 1.8993 5.5283 1.062 3.1578 1115.712
MCD-estimated:
MDC-0.975-Mean: 13.7227 1.738 2.4541 17.0805 106.3171 2.8412 2.9763 0.2861 1.8193 5.5912 1.0661 3.1676 1148.781
MDC-0.750-Mean: 13.7428 1.7492 2.439 16.9744 105.5385 2.8221 2.9269 0.2821 1.8418 5.3626 1.0538 3.1821 1123.718
MDC-0.500-Mean: 13.7372 1.7428 2.4607 17.1977 106.4884 2.8447 2.9588 0.2905 1.8221 5.5102 1.0614 3.157 1129.186
Class2: n= 48
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0.2812 0.0637 0.0240 0.2514 -0.4859 0.0398 0.0118 0.0025 0.0816 0.4293 -0.0021 0.0191 -5.4347
[2,] 0.0637 1.1835 0.0036 0.2089 -2.0731 -0.0624 -0.0899 0.0193 -0.0994 -0.4078 0.0099 0.0021 0.3642
[3,] 0.0240 0.0036 0.0341 0.3163 0.4250 0.0310 0.0150 -0.0005 0.0146 0.0534 0.0038 0.0113 -3.1312
[4,] 0.2514 0.2089 0.3163 5.0993 3.9202 0.2938 0.1799 -0.0048 0.2432 0.8382 0.0071 0.0256 -25.6472
[5,] -0.4859 -2.0731 0.4250 3.9202 118.6024 -0.1541 1.8180 -0.6837 0.6836 2.6223 0.0057 -0.6609 238.0120
[6,] 0.0398 -0.0624 0.0310 0.2938 -0.1541 0.1274 0.0250 0.0145 0.0905 0.2771 -0.0011 0.0195 1.7637
[7,] 0.0118 -0.0899 0.0150 0.1799 1.8180 0.0250 0.0861 -0.0231 0.0490 0.2489 -0.0098 -0.0343 -8.3254
[8,] 0.0025 0.0193 -0.0005 -0.0048 -0.6837 0.0145 -0.0231 0.0154 0.0087 0.0075 0.0022 0.0104 2.8891
[9,] 0.0816 -0.0994 0.0146 0.2432 0.6836 0.0905 0.0490 0.0087 0.1671 0.6471 -0.0197 -0.0143 9.1982
[10,] 0.4293 -0.4078 0.0534 0.8382 2.6223 0.2771 0.2489 0.0075 0.6471 5.3405 -0.1504 -0.0648 32.0230
[11,] -0.0021 0.0099 0.0038 0.0071 0.0057 -0.0011 -0.0098 0.0022 -0.0197 -0.1504 0.0131 0.0113 0.0311
[12,] 0.0191 0.0021 0.0113 0.0256 -0.6609 0.0195 -0.0343 0.0104 -0.0143 -0.0648 0.0113 0.0740 6.1259
[13,] -5.4347 0.3642 -3.1312 -25.6472 238.0120 1.7637 -8.3254 2.8891 9.1982 32.0230 0.0311 6.1259 13247.3293
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 1.0000 0.1105 0.2451 0.2099 -0.0841 0.2104 0.0761 0.0382 0.3766 0.3504 -0.0341 0.1322 -0.0891
[2,] 0.1105 1.0000 0.0181 0.0850 -0.1750 -0.1607 -0.2816 0.1428 -0.2234 -0.1622 0.0797 0.0072 0.0029
[3,] 0.2451 0.0181 1.0000 0.7585 0.2113 0.4704 0.2771 -0.0204 0.1938 0.1252 0.1805 0.2254 -0.1473
[4,] 0.2099 0.0850 0.7585 1.0000 0.1594 0.3645 0.2714 -0.0171 0.2634 0.1606 0.0276 0.0417 -0.0987
[5,] -0.0841 -0.1750 0.2113 0.1594 1.0000 -0.0396 0.5688 -0.5057 0.1535 0.1042 0.0046 -0.2230 0.1899
[6,] 0.2104 -0.1607 0.4704 0.3645 -0.0396 1.0000 0.2389 0.3263 0.6200 0.3359 -0.0261 0.2004 0.0429
[7,] 0.0761 -0.2816 0.2771 0.2714 0.5688 0.2389 1.0000 -0.6345 0.4080 0.3669 -0.2931 -0.4297 -0.2464
[8,] 0.0382 0.1428 -0.0204 -0.0171 -0.5057 0.3263 -0.6345 1.0000 0.1718 0.0263 0.1531 0.3066 0.2022
[9,] 0.3766 -0.2234 0.1938 0.2634 0.1535 0.6200 0.4080 0.1718 1.0000 0.6849 -0.4218 -0.1286 0.1955
[10,] 0.3504 -0.1622 0.1252 0.1606 0.1042 0.3359 0.3669 0.0263 0.6849 1.0000 -0.5686 -0.1030 0.1204
[11,] -0.0341 0.0797 0.1805 0.0276 0.0046 -0.0261 -0.2931 0.1531 -0.4218 -0.5686 1.0000 0.3643 0.0024
[12,] 0.1322 0.0072 0.2254 0.0417 -0.2230 0.2004 -0.4297 0.3066 -0.1286 -0.1030 0.3643 1.0000 0.1956
[13,] -0.0891 0.0029 -0.1473 -0.0987 0.1899 0.0429 -0.2464 0.2022 0.1955 0.1204 0.0024 0.1956 1.0000
Median: 13.187 3.0508 2.4547 21.4506 97.5229 1.7777 0.8029 0.448 1.2204 7.7863 0.6726 1.7161 625.3559
Mean: 13.1538 3.3338 2.4371 21.4167 99.3125 1.6788 0.7815 0.4475 1.1535 7.3962 0.6827 1.6835 629.8958
MCD-estimated:
MDC-0.975-Mean: 13.2191 3.603 2.4088 21.1515 97.1212 1.6282 0.6506 0.4824 1.1158 7.4045 0.6864 1.753 648.3333
MDC-0.750-Mean: 13.2 3.2744 2.4332 21.25 100.8529 1.6526 0.8224 0.4365 1.1447 7.5238 0.6759 1.6376 621.9118
MDC-0.500-Mean: 13.1945 3.3903 2.4242 21.303 100.8788 1.6648 0.8345 0.4358 1.1585 7.6561 0.6748 1.5973 619.2424
Measures:
Mah.Dist: 10.78
Mah.Dist-MCD-0.975: 10.2546
Mah.Dist-MCD-0.750: 10.9787
Mah.Dist-MCD-0.500: 11.3133
Zuletzt geändert am 17.02.2013
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