Adult {arules} | R Documentation

## Adult Data Set

### Description

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.

### Usage

    data("Adult")
    data("AdultUCI")

### Format

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`.

### Details

The “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 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).

### Source

http://www.ics.uci.edu/~mlearn/MLRepository.html

### References

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.

### Examples

    data("AdultUCI")
    dim(AdultUCI)
    AdultUCI[1:2,]

    ## 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 <- as(AdultUCI, "transactions")
    Adult

[Package arules version 0.6-3 Index]