Adult(arules)
Adult()所属R语言包：arules
Adult Data Set
成人数据集
译者：生物统计家园网 机器人LoveR
描述----------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.
AdultUCI数据集包含的“成人”数据库（最初被称为“人口普查收入”数据库）数据框格式的问卷调查的数据。 Adult数据集包含的数据已经准备好，并强制转换为transactions使用arules。
用法----------Usage----------
data("Adult")
data("AdultUCI")
格式----------Format----------
The AdultUCI data set contains a data frame with 48842 observations on the following 15 variables.
AdultUCI数据集包含一个数据框48842以下15个变量的观察。
age a numeric vector.
年龄的数值向量。
workclass a factor with levels Federal-gov, Local-gov, Never-worked, Private, Self-emp-inc, Self-emp-not-inc, State-gov,
workclass的因素与水平Federal-gov，Local-gov，Never-worked，Private，Self-emp-inc，Self-emp-not-inc，State-gov，
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 <
教育的有序因素与水平Preschool1st-4th5th-6th7th-8th9th10th11th<12thHS-gradProf-schoolAssoc-acdmAssoc-vocSome-college<X ><Bachelors
education-num a numeric vector.
教育NUM一个数值向量。
marital-status a factor with levels Divorced, Married-AF-spouse, Married-civ-spouse, Married-spouse-absent, Never-married,
婚姻状况的因素与水平Divorced，Married-AF-spouse，Married-civ-spouse，Married-spouse-absent，Never-married，
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
职业与水平的一个因素Adm-clerical，Armed-Forces，Craft-repair，Exec-managerial，Farming-fishing，Handlers-cleaners，Machine-op-inspct，<X >，Other-service，Priv-house-serv，Prof-specialty，Protective-serv，Sales，和
relationship a factor with levels Husband, Not-in-family, Other-relative, Own-child,
关系的因素与水平Husband，Not-in-family，Other-relative，Own-child，
race a factor with levels Amer-Indian-Eskimo, Asian-Pac-Islander, Black, Other, and
水平Amer-Indian-Eskimo，Asian-Pac-Islander，Black，Other，和比赛的一个因素
sex a factor with levels Female and Male.
水平Female和Male性的因素。
capital-gain a numeric vector.
资本获得一个数值向量。
capital-loss a numeric vector.
资本损失的数值向量。
fnlwgt a numeric vector.
fnlwgt一个数值向量。
hours-per-week a numeric vector.
小时，每星期一个数值向量。
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,
为母语的国家的一个因素水平Cambodia，Canada，China，Columbia，Cuba，Dominican-Republic，Ecuador， El-Salvador，England，France，Germany，Greece，Guatemala，Haiti，Holand-Netherlands，<X >，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，
income an ordered factor with levels small <
收入的有序因素与水平small<
Details
详细信息----------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).
“成人”的数据库，从人口普查局发现在http://www.census.gov/ftp/pub/DES/www/welcome.html在1994年罗尼Kohavi和巴里·贝克尔，数据挖掘和可视化的数据库中提取， Silicon Graphics公司。它最初是用来预测收入是否超过美元50K/yr，根据普查数据。我们添加了属性income同级别small和large（> 50000）。
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:
我们准备的数据部分中的实施例所示，设置为关联挖掘。我们删除了连续属性fnlwgt（最终重量）。我们也消除了education-num，因为它只是一个数字表示的属性education。其他4个连续的属性映射到序属性，如下所示：
age cut into levels Young (0-25), Middle-aged (26-45), Senior (46-65) and
年龄切成Young（0-25），Middle-aged（26-45），Senior（46-65级）和
hours-per-week cut into levels Part-time (0-25), Full-time (25-40), Over-time (40-60) and
小时，每星期切成水平Part-time（0-25），Full-time（25-40），Over-time（40-60）
capital-gain and capital-loss each cut into levels None (0), Low (0 < median of the values greater zero < max) and
资本增益和资本损失每个切成水平None（0），Low（0 <中位数的值大于零的<最大）和
源----------Source----------
http://www.ics.uci.edu/~mlearn/MLRepository.html
参考文献----------References----------
UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Department of Information and Computer Science.
of Naive-Bayes Classifiers: a Decision-Tree Hybrid. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining.
实例----------Examples----------
data("AdultUCI")
dim(AdultUCI)
AdultUCI[1:2,]
## remove attributes[＃删除属性]
AdultUCI[["fnlwgt"]] <- NULL
AdultUCI[["education-num"]] <- NULL
## map metric attributes[＃公制的属性映射]
AdultUCI[["age"]] <- cut(AdultUCI[["age"]], c(15,25,45,65,100),
labels = c("Young", "Middle-aged", "Senior", "Old"), ordered=TRUE)
AdultUCI[["hours-per-week"]] <- cut(AdultUCI[["hours-per-week"]],
c(0,25,40,60,168), labels = c("Part-time", "Full-time",
"Over-time", "Workaholic"), ordered=TRUE)
AdultUCI[["capital-gain"]] <- cut(AdultUCI[["capital-gain"]], c(-Inf,0,
median(AdultUCI[["capital-gain"]][AdultUCI[["capital-gain"]]>0]),Inf),
labels = c("None", "Low", "High"), ordered=TRUE)
AdultUCI[["capital-loss"]] <- cut(AdultUCI[["capital-loss"]], c(-Inf,0,
median(AdultUCI[[ "capital-loss"]][AdultUCI[[ "capital-loss"]]>0]),Inf),
labels = c("none", "low", "high"), ordered=TRUE)
## create transactions[＃创建交易]
Adult <- as(AdultUCI, "transactions")
Adult
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-12 11:04:07
