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Uni Köln
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für Wirtschafts- und Sozialstatistik > Institut
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Mosler > Prof. Mosler > Datenportal
Datenportal des Lehrstuhls für Statistik und Ökonometrie
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Glass (F vs NF) data
The data set (and description) can be downloaded here:
http://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data
Description:
1. Title: Glass Identification Database
2. Sources:
(a) Creator: B. German
-- Central Research Establishment
Home Office Forensic Science Service
Aldermaston, Reading, Berkshire RG7 4PN
(b) Donor: Vina Spiehler, Ph.D., DABFT
Diagnostic Products Corporation
(213) 776-0180 (ext 3014)
(c) Date: September, 1987
3. Past Usage:
-- Rule Induction in Forensic Science
-- Ian W. Evett and Ernest J. Spiehler
-- Central Research Establishment
Home Office Forensic Science Service
Aldermaston, Reading, Berkshire RG7 4PN
-- Unknown technical note number (sorry, not listed here)
-- General Results: nearest neighbor held its own with respect to the
rule-based system
4. Relevant Information:n
Vina conducted a comparison test of her rule-based system, BEAGLE, the
nearest-neighbor algorithm, and discriminant analysis. BEAGLE is
a product available through VRS Consulting, Inc.; 4676 Admiralty Way,
Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189.
In determining whether the glass was a type of "float" glass or not,
the following results were obtained (# incorrect answers):
Type of Sample Beagle NN DA
Windows that were float processed (87) 10 12 21
Windows that were not: (76) 19 16 22
The study of classification of types of glass was motivated by
criminological investigation. At the scene of the crime, the glass left
can be used as evidence...if it is correctly identified!
5. Number of Instances: 214
6. Number of Attributes: 10 (including an Id#) plus the class attribute
-- all attributes are continuously valued
7. Attribute Information:
1. Id number: 1 to 214
2. RI: refractive index
3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as
are attributes 4-10)
4. Mg: Magnesium
5. Al: Aluminum
6. Si: Silicon
7. K: Potassium
8. Ca: Calcium
9. Ba: Barium
10. Fe: Iron
11. Type of glass: (class attribute)
-- 1 building_windows_float_processed
-- 2 building_windows_non_float_processed
-- 3 vehicle_windows_float_processed
-- 4 vehicle_windows_non_float_processed (none in this database)
-- 5 containers
-- 6 tableware
-- 7 headlamps
8. Missing Attribute Values: None
Summary Statistics:
Attribute: Min Max Mean SD Correlation with class
2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642
3. Na: 10.73 17.38 13.4079 0.8166 0.5030
4. Mg: 0 4.49 2.6845 1.4424 -0.7447
5. Al: 0.29 3.5 1.4449 0.4993 0.5988
6. Si: 69.81 75.41 72.6509 0.7745 0.1515
7. K: 0 6.21 0.4971 0.6522 -0.0100
8. Ca: 5.43 16.19 8.9570 1.4232 0.0007
9. Ba: 0 3.15 0.1750 0.4972 0.5751
10. Fe: 0 0.51 0.0570 0.0974 -0.1879
9. Class Distribution: (out of 214 total instances)
-- 163 Window glass (building windows and vehicle windows)
-- 87 float processed
-- 70 building windows
-- 17 vehicle windows
-- 76 non-float processed
-- 76 building windows
-- 0 vehicle windows
-- 51 Non-window glass
-- 13 containers
-- 9 tableware
-- 29 headlamps
Citation Request:
Please refer to the repository http://archive.ics.uci.edu/ml (see citation policy).
See also Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
Descriptive statistics:
Dataset= glass : n= 146 , d= 9
Class1: n= 70
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 0.0000 0.0006 0.0002 -0.0005 -0.0010 -0.0004 0.0011 0.0000 0.0000
[2,] 0.0006 0.2493 0.0479 -0.0816 -0.2295 -0.0762 0.0962 0.0062 -0.0150
[3,] 0.0002 0.0479 0.0610 -0.0267 -0.0707 -0.0271 0.0199 0.0001 -0.0043
[4,] -0.0005 -0.0816 -0.0267 0.0746 0.0972 0.0440 -0.1163 0.0019 0.0023
[5,] -0.0010 -0.2295 -0.0707 0.0972 0.3243 0.0999 -0.2234 -0.0098 0.0098
[6,] -0.0004 -0.0762 -0.0271 0.0440 0.0999 0.0462 -0.0894 -0.0030 0.0028
[7,] 0.0011 0.0962 0.0199 -0.1163 -0.2234 -0.0894 0.3304 -0.0016 0.0016
[8,] 0.0000 0.0062 0.0001 0.0019 -0.0098 -0.0030 -0.0016 0.0070 -0.0004
[9,] 0.0000 -0.0150 -0.0043 0.0023 0.0098 0.0028 0.0016 -0.0004 0.0079
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 1.0000 0.5635 0.4004 -0.7268 -0.8072 -0.8213 0.8460 0.0518 -0.1672
[2,] 0.5635 1.0000 0.3880 -0.5981 -0.8072 -0.7101 0.3352 0.1481 -0.3370
[3,] 0.4004 0.3880 1.0000 -0.3950 -0.5024 -0.5097 0.1404 0.0070 -0.1959
[4,] -0.7268 -0.5981 -0.3950 1.0000 0.6248 0.7498 -0.7406 0.0828 0.0937
[5,] -0.8072 -0.8072 -0.5024 0.6248 1.0000 0.8163 -0.6825 -0.2058 0.1937
[6,] -0.8213 -0.7101 -0.5097 0.7498 0.8163 1.0000 -0.7241 -0.1644 0.1482
[7,] 0.8460 0.3352 0.1404 -0.7406 -0.6825 -0.7241 1.0000 -0.0334 0.0315
[8,] 0.0518 0.1481 0.0070 0.0828 -0.2058 -0.1644 -0.0334 1.0000 -0.0600
[9,] -0.1672 -0.3370 -0.1959 0.0937 0.1937 0.1482 0.0315 -0.0600 1.0000
Median: 1.5179 13.0924 3.5182 1.2406 72.8072 0.5326 8.625 0.0069 0.0537
Mean: 1.5187 13.2423 3.5524 1.1639 72.6191 0.4474 8.7973 0.0127 0.057
MCD-estimated:
MDC-0.975-Mean: 1.5185 13.3125 3.596 1.149 72.636 0.4555 8.6903 0 0
MDC-0.750-Mean: 1.5186 13.2841 3.5815 1.1715 72.6378 0.4583 8.7046 0 0
MDC-0.500-Mean: 1.5186 13.2841 3.5815 1.1715 72.6378 0.4583 8.7046 0 0
Class2: n= 76
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 0.0000 -0.0010 -0.0036 -0.0005 -0.0018 -0.0005 0.0068 0.0005 0.0001
[2,] -0.0010 0.4411 0.2792 0.0050 -0.0343 -0.0055 -0.5766 -0.0994 -0.0122
[3,] -0.0036 0.2792 1.4778 0.1075 0.2421 0.1461 -2.1240 -0.1285 -0.0165
[4,] -0.0005 0.0050 0.1075 0.1013 -0.0196 0.0435 -0.2718 0.0297 -0.0004
[5,] -0.0018 -0.0343 0.2421 -0.0196 0.5250 0.0274 -0.6237 -0.1164 -0.0186
[6,] -0.0005 -0.0055 0.1461 0.0435 0.0274 0.0457 -0.2638 0.0029 0.0007
[7,] 0.0068 -0.5766 -2.1240 -0.2718 -0.6237 -0.2638 3.6927 0.1769 0.0355
[8,] 0.0005 -0.0994 -0.1285 0.0297 -0.1164 0.0029 0.1769 0.1313 0.0090
[9,] 0.0001 -0.0122 -0.0165 -0.0004 -0.0186 0.0007 0.0355 0.0090 0.0113
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 1.0000 -0.3899 -0.7698 -0.3978 -0.6598 -0.5756 0.9327 0.3863 0.2022
[2,] -0.3899 1.0000 0.3457 0.0234 -0.0712 -0.0388 -0.4518 -0.4129 -0.1726
[3,] -0.7698 0.3457 1.0000 0.2777 0.2748 0.5624 -0.9092 -0.2918 -0.1277
[4,] -0.3978 0.0234 0.2777 1.0000 -0.0848 0.6400 -0.4443 0.2575 -0.0122
[5,] -0.6598 -0.0712 0.2748 -0.0848 1.0000 0.1767 -0.4480 -0.4434 -0.2407
[6,] -0.5756 -0.0388 0.5624 0.6400 0.1767 1.0000 -0.6424 0.0376 0.0324
[7,] 0.9327 -0.4518 -0.9092 -0.4443 -0.4480 -0.6424 1.0000 0.2540 0.1734
[8,] 0.3863 -0.4129 -0.2918 0.2575 -0.4434 0.0376 0.2540 1.0000 0.2343
[9,] 0.2022 -0.1726 -0.1277 -0.0122 -0.2407 0.0324 0.1734 0.2343 1.0000
Median: 1.5173 13.151 3.4867 1.4489 72.7502 0.5785 8.3344 0.0108 0.0641
Mean: 1.5186 13.1117 3.0021 1.4082 72.598 0.5211 9.0737 0.0503 0.0797
MCD-estimated:
MDC-0.975-Mean: 1.5181 13.113 3.1188 1.4153 72.723 0.5293 8.8705 0 0
MDC-0.750-Mean: 1.5181 13.113 3.1188 1.4153 72.723 0.5293 8.8705 0 0
MDC-0.500-Mean: 1.517 13.1778 3.6248 1.4789 72.73 0.5961 8.155 0 0.0591
Measures:
Mah.Dist: 1.3286
Mah.Dist-MCD-0.975: 1.2972
Mah.Dist-MCD-0.750: 1.2629
Mah.Dist-MCD-0.500: 1.2972
Zuletzt geändert am 17.02.2013
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