glass: Glass Identification Database
In kknn: Weighted k-Nearest Neighbors

Description Usage Format Source Examples

A data frame with 214 observations, where the problem is to predict the type of glass in terms of their oxide content (i.e. Na, Fe, K, etc). 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!

1	data(glass)

A data frame with 214 observations on the following 11 variables.

Id: Id number.
RI: Refractive index.
Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10).
Mg: Magnesium.
Al: Aluminum.
Si: Silicon.
K: Potassium.
Ca: Calcium.
Ba: Barium.
Fe: Iron.
Type: 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

Creator: B. German, Central Research Establishment, Home Office Forensic Science Service, Aldermaston, Reading, Berkshire RG7 4PN
Donor: Vina Spiehler, Ph.D., DABFT, Diagnostic Products Corporation

The data have been taken from the UCI Machine Learning Database Repository
http://www.ics.uci.edu/~mlearn/MLRepository.html
and were converted to R format by klaus.schliep@gmail.com.

1 2	data(glass) str(glass)

'data.frame':	214 obs. of  11 variables:
 $ Id  : int  1 2 3 4 5 6 7 8 9 10 ...
 $ RI  : num  1.52 1.52 1.52 1.52 1.52 ...
 $ Na  : num  13.6 13.9 13.5 13.2 13.3 ...
 $ Mg  : num  4.49 3.6 3.55 3.69 3.62 3.61 3.6 3.61 3.58 3.6 ...
 $ Al  : num  1.1 1.36 1.54 1.29 1.24 1.62 1.14 1.05 1.37 1.36 ...
 $ Si  : num  71.8 72.7 73 72.6 73.1 ...
 $ K   : num  0.06 0.48 0.39 0.57 0.55 0.64 0.58 0.57 0.56 0.57 ...
 $ Ca  : num  8.75 7.83 7.78 8.22 8.07 8.07 8.17 8.24 8.3 8.4 ...
 $ Ba  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Fe  : num  0 0 0 0 0 0.26 0 0 0 0.11 ...
 $ Type: Factor w/ 6 levels "1","2","3","5",..: 1 1 1 1 1 1 1 1 1 1 ...