| Adult {arules} | R Documentation |
The Adult data set contains the questionnaire data of the
“Adult” database (originally called the “Census Income”
Database) formatted as a data.frame prepared for use with
arules. The Adult_transactions data set contains the data
already coerced to transactions.
data("Adult")
data("Adult_transactions")
Adult contains a data.frame with 48842 observations on
the following 14 variables:
middle-aged, old,
senior, youngFederal-gov,
Local-gov, Never-worked, Private,
Self-emp-inc, Self-emp-not-inc, State-gov,
Without-pay10th, 11th,
12th, 1st-4th, 5th-6th, 7th-8th,
9th, Assoc-acdm, Assoc-voc, Bachelors,
Doctorate, HS-grad, Masters,
Preschool, Prof-school, Some-college1, 10,
11, 12, 13, 14, 15, 16,
2, 3, 4, 5, 6, 7,
8, 9Divorced,
Married-AF-spouse, Married-civ-spouse,
Married-spouse-absent, Never-married,
Separated, WidowedAdm-clerical,
Armed-Forces, Craft-repair, Exec-managerial,
Farming-fishing, Handlers-cleaners,
Machine-op-inspct, Other-service,
Priv-house-serv, Prof-specialty,
Protective-serv, Sales, Tech-support,
Transport-movingHusband,
Not-in-family, Other-relative, Own-child,
Unmarried, WifeAmer-Indian-Eskimo,
Asian-Pac-Islander, Black, Other,
WhiteFemale, Malehigh, medium,
none, smallmedium, none,
smallfull-time,
half-time, overtime, too-manyCambodia,
Canada, China, Columbia, Cuba,
Dominican-Republic, Ecuador, El-Salvador,
England, France, Germany, Greece,
Guatemala, Haiti, Holand-Netherlands,
Honduras, Hong, Hungary, India,
Iran, Ireland, Italy, Jamaica,
Japan, Laos, Mexico, Nicaragua,
Outlying-US(Guam-USVI-etc), Peru,
Philippines, Poland, Portugal,
Puerto-Rico, Scotland, South, Taiwan,
Thailand, Trinadad&Tobago, United-States,
Vietnam, Yugoslaviasmall, largeThe “Adult” database was extracted from the census bureau database found at http://www.census.gov/ftp/pub/DES/www/welcome.html in 1994 by Ronny Kohavi and Barry Becker, Data Mining and Visualization, Silicon Graphics. It was originally used to predict whether income exceeds $50K/yr based on census data.
To prepare the data set for association mining, we removed the
continuous attribute fnlwgt (final weight) and added the
attribute salary with levels ‘small’ and ‘large’
($>$$50K/yr). The original data contained 5 more continuous
attributes (age, education-num, capital-gain,
capital-loss and hours-per-week) which we coded using
discrete values.
http://www.ics.uci.edu/~mlearn/MLRepository.html
Blake, C.L. & Merz, C.J. (1998). UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Department of Information and Computer Science.
The data set was first cited in Kohavi, R. (1996). Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining.
data("Adult")
dim(Adult)
Adult[1:2, 1:4]
data("Adult_transactions")
Adult_transactions