Glass Identification

Donated on 8/31/1987

From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)

Dataset Characteristics


Subject Area

Physical Science

Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

Additional Information

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!

Has Missing Values?


Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
RIFeatureContinuousrefractive indexno
NaFeatureContinuousSodiumweight percent in corresponding oxideno
MgFeatureContinuousMagnesiumweight percent in corresponding oxideno
AlFeatureContinuousAluminumweight percent in corresponding oxideno
SiFeatureContinuousSiliconweight percent in corresponding oxideno
KFeatureContinuousPotassiumweight percent in corresponding oxideno
CaFeatureContinuousCalciumweight percent in corresponding oxideno
BaFeatureContinuousBariumweight percent in corresponding oxideno
FeFeatureContinuousIronweight percent in corresponding oxideno

0 to 10 of 11

Additional Variable 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

Class Labels

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

Baseline Model Performance

Papers Citing this Dataset

Search Based Code Generation for Machine Learning Programs

By Muhammad Malik, Muhammad Nawaz, Nimrah Mustafa, Junaid Siddiqui. 2018

Published in ArXiv.

MIME-KNN: Improve KNN Classifier Performance Include Classification Accuracy and Time Consumption

By Taizhang Shang, Xiang Xia, Jun Zheng. 2018

Published in DEStech Transactions on Computer Science and Engineering.

Object Classification Using Support Vector Machines with Kernel-based Data Preprocessing

By Krzysztof Adamiak, Piotr Duch, Krzysztof Ślot. 2016

Published in Image Processing & Communications.

Three Similarity Measures between One-Dimensional Data Sets

By Luis Gonzalez-Abril, José Gavilán, Francisco Morente. 2014

Published in Revista Colombiana de Estadística.

An Outlier Mining Algorithm Based on Attribute Entropy

By Ming-jian Zhou, Jun-Cai Tao. 2011

Published in Procedia Environmental Sciences.

0 to 5 of 7

7 citations




B. German


By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy