Adult Data Set — Adult • arules

The AdultUCI data set contains the questionnaire data of the Adult database (originally called the Census Income Database) formatted as a data.frame. The Adult data set contains the data already prepared and coerced to transactions for use with arules.

Format

Adult is an object of class transactions with 48842 transactions and 115 items. See below for details.

The AdultUCI data set contains a data frame with 48842 observations on the following 15 variables.

age: a numeric vector.
workclass: a factor with levels Federal-gov, Local-gov, Never-worked, Private, Self-emp-inc, Self-emp-not-inc, State-gov, and Without-pay.
education: an ordered factor with levels Preschool < 1st-4th < 5th-6th < 7th-8th < 9th < 10th < 11th < 12th < HS-grad < Prof-school < Assoc-acdm < Assoc-voc < Some-college < Bachelors < Masters < Doctorate.
education-num: a numeric vector.
marital-status: a factor with levels Divorced, Married-AF-spouse, Married-civ-spouse, Married-spouse-absent, Never-married, Separated, and Widowed.
occupation: a factor with levels Adm-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, and Transport-moving.
relationship: a factor with levels Husband, Not-in-family, Other-relative, Own-child, Unmarried, and Wife.
race: a factor with levels Amer-Indian-Eskimo, Asian-Pac-Islander, Black, Other, and White.
sex: a factor with levels Female and Male.
capital-gain: a numeric vector.
capital-loss: a numeric vector.
fnlwgt: a numeric vector.
hours-per-week: a numeric vector.
native-country: a factor with levels Cambodia, 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, and Yugoslavia.
income: an ordered factor with levels small < large.

Source

https://archive.ics.uci.edu/

Details

The Adult database was extracted from the census bureau database found at https://www.census.gov/ in 1994 by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). It was originally used to predict whether income exceeds USD 50K/yr based on census data. We added the attribute income with levels small and large (>50K).

We prepared the data set for association mining as shown in the section Examples. We removed the continuous attribute fnlwgt (final weight). We also eliminated education-num because it is just a numeric representation of the attribute education. The other 4 continuous attributes we mapped to ordinal attributes as follows:

age: cut into levels Young (0-25), Middle-aged (26-45), Senior (46-65) and Old (66+)
hours-per-week: cut into levels Part-time (0-25), Full-time (25-40), Over-time (40-60) and Too-much (60+)
capital-gain and capital-loss: each cut into levels None (0), Low (0 < median of the values greater zero < max) and High (>=max)

References

A. Asuncion & D. J. Newman (2007): 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.

Author

Michael Hahsler

Examples


data("AdultUCI")
dim(AdultUCI)
#> [1] 48842    15
AdultUCI[1:2, ]
#>   age        workclass fnlwgt education education-num     marital-status
#> 1  39        State-gov  77516 Bachelors            13      Never-married
#> 2  50 Self-emp-not-inc  83311 Bachelors            13 Married-civ-spouse
#>        occupation  relationship  race  sex capital-gain capital-loss
#> 1    Adm-clerical Not-in-family White Male         2174            0
#> 2 Exec-managerial       Husband White Male            0            0
#>   hours-per-week native-country income
#> 1             40  United-States  small
#> 2             13  United-States  small

## remove attributes
AdultUCI[["fnlwgt"]] <- NULL
AdultUCI[["education-num"]] <- NULL

## map metric attributes
AdultUCI[["age"]] <- ordered(cut(AdultUCI[["age"]], c(15, 25, 45, 65, 100)),
  labels = c("Young", "Middle-aged", "Senior", "Old")
)

AdultUCI[["hours-per-week"]] <- ordered(
  cut(
    AdultUCI[["hours-per-week"]],
    c(0, 25, 40, 60, 168)
  ),
  labels = c("Part-time", "Full-time", "Over-time", "Workaholic")
)

AdultUCI[["capital-gain"]] <- ordered(cut(
  AdultUCI[["capital-gain"]],
  c(
    -Inf, 0, median(AdultUCI[["capital-gain"]][AdultUCI[["capital-gain"]] > 0]),
    Inf
  )
), labels = c("None", "Low", "High"))

AdultUCI[["capital-loss"]] <- ordered(cut(
  AdultUCI[["capital-loss"]],
  c(
    -Inf, 0, median(AdultUCI[["capital-loss"]][AdultUCI[["capital-loss"]] > 0]),
    Inf
  )
), labels = c("None", "Low", "High"))

## create transactions
Adult <- transactions(AdultUCI)
Adult
#> transactions in sparse format with
#>  48842 transactions (rows) and
#>  115 items (columns)