Adult Data Set

Description

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 prepared and coerced to transactions.

Usage

data("Adult")
data("Adult_transactions")

Format

The Adult 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).

To prepare the data set for association mining, 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("Adult")
dim(Adult)
Adult[1:2,]

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

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

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

Adult[[ "capital-gain"]] <- ordered(cut(Adult[[ "capital-gain"]],
      c(-Inf,0,median(Adult[[ "capital-gain"]][Adult[[ "capital-gain"]]>0]),1000000)),
    labels = c("None", "Low", "High"))

Adult[[ "capital-loss"]] <- ordered(cut(Adult[[ "capital-loss"]],
      c(-Inf,0,
        median(Adult[[ "capital-loss"]][Adult[[ "capital-loss"]]>0]),1000000)),
    labels = c("None", "Low", "High"))

### create transactions
Adult_transactions <- as(Adult, "transactions")
Adult_transactions