十大数据挖掘算法的R语言实现

时间：2022-06-25 08:55:10

iris数据集 
iris以鸢尾花的特征作为数据来源，常用在分类操作中。该数据集由3种不同类型的鸢尾花的50个样本数据构成。其中的一个种类与另外两个种类是线性可分离的，后两个种类是非线性可分离的。

library(ggplot2)
summary(iris)
qplot(Petal.Length, Petal.Width, data=iris, color=Species)
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1：C5.0决策树 
先加载所需要的包

library(C50)
library(printr)
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对iris数据集进行抽样，获得训练样本和测试样本

train.indeces <- sample(1:nrow(iris), 100)
iris.train <- iris[train.indeces, ]
iris.test <- iris[-train.indeces, ]
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利用C5.0函数对训练样本进行模型训练

model <- C5.0(Species ~ ., data = iris.train)
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对测试样本进行预测

results <- predict(object = model, newdata = iris.test, type = "class")
confusion_matrix=table(results, iris.test$Species)
confusion_matrix
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计算错误率

error=1-sum(diag(confusion_matrix))/nrow(iris.test)
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预测错误率为0.12 
2：K-means 
模型建立

library(stats)
library(printr)
model <- kmeans(x = subset(iris, select = -Species), centers = 3)
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分类性能测试

table(model$cluster, iris$Species)
/   setosa  versicolor  virginica
1   33  0   0
2   17  4   0
3   0   46  50
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3：Support Vector Machines导入包library(e1071)library(printr) 对iris数据集进行抽样，获得训练样本和测试样本train.indeces <- sample(1:nrow(iris), 100)iris.train <- iris[train.indeces, ]iris.test <- iris[-train.indeces, ]
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利用C5.0函数对训练样本进行模型训练

model <- svm(Species ~ ., data = iris.train)

对测试样本进行预测

results <- predict(object = model, newdata = iris.test, type = "class")
confusion_matrix=table(results, iris.test$Species)
confusion_matrix
results/    setosa  versicolor  virginica
setosa  12  0   0
versicolor  0   19  0
virginica   0   1   18

计算错误率
error=1-sum(diag(confusion_matrix))/nrow(iris.test)
预测错误率为0.02
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4：Apriori导入包和数据集library(arules)library(printr)data("Adult")训练模型rules <- apriori(Adult,    parameter = list(support = 0.4, confidence = 0.7),    appearance = list(rhs = c("race=White", "sex=Male"), default = "lhs"))获得前五的关联关系rules.sorted <- sort(rules, by = "lift")top5.rules <- head(rules.sorted, 5)as(top5.rules, "data.frame")    rules   support confidence  lift2   {relationship=Husband} => {sex=Male}    0.4036485   0.9999493   1.49585112  {marital-status=Married-civ-spouse,relationship=Husband} => {sex=Male}  0.4034028   0.9999492   1.4958513   {marital-status=Married-civ-spouse} => {sex=Male}   0.4074157   0.8891818   1.3301514   {marital-status=Married-civ-spouse} => {race=White} 0.4105892   0.8961080   1.04802719  {workclass=Private,native-country=United-States} => {race=White}    0.5433848   0.8804113   1.029669
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5:EM算法library(mclust)library(printr)model <- Mclust(subset(iris, select = -Species))table(model$classification, iris$Species)/ setosa versicolor virginica1 50 0 02 0 50 50
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6：PageRankPageRank用来计算图中各点的相关程度，其原理是马尔科夫链library(igraph)library(dplyr)library(printr)生成随机的网络图g <- random.graph.game(n = 10, p.or.m = 1/4, directed = TRUE)plot(g)对每个节点计算rankpage值pr <- page.rank(g)$vectordf <- data.frame(Object = 1:10, PageRank = pr)arrange(df, desc(PageRank))Object PageRank10 0.17686557 0.13693881 0.12638764 0.11981672 0.11618249 0.08912666 0.08475798 0.07932865 0.03901473 0.0315813
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7:adaboostlibrary(adabag)library(printr)train.indeces <- sample(1:nrow(iris), 100)iris.train <- iris[train.indeces, ]iris.test <- iris[-train.indeces, ]模型训练model <- boosting(Species ~ ., data = iris.train)训练结果results <- predict(object = model, newdata = iris.test, type = "class")results$confusionPredicted Class/Observed Class  setosa  versicolor  virginicasetosa  15  0   0versicolor  0   18  4virginica   0   0   13
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8:kNNlibrary(class)library(printr)train.indeces <- sample(1:nrow(iris), 100)iris.train <- iris[train.indeces, ]iris.test <- iris[-train.indeces, ]模型训练results <- knn(train = subset(iris.train, select = -Species),    test = subset(iris.test, select = -Species),    cl = iris.train$Species)分类效果table(results, iris.test$Species)results/    setosa  versicolor  virginicasetosa  22  0   0versicolor  0   10  0virginica   0   1   17
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9:naive bayeslibrary(e1071)library(printr)train.indeces <- sample(1:nrow(iris), 100)iris.train <- iris[train.indeces, ]iris.test <- iris[-train.indeces, ]训练集训练模型model <- naiveBayes(x = subset(iris.train, select=-Species), y = iris.train$Species)测试集预测效果results <- predict(object = model, newdata = iris.test,type ="class")table(results, iris.test$Species)results/    setosa  versicolor  virginicasetosa  18  0   0versicolor  0   17  0virginica   0   4   11
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10:cart 
library(rpart) 
library(printr) 
train.indeces <- sample(1:nrow(iris), 100) 
iris.train <- iris[train.indeces, ] 
iris.test <- iris[-train.indeces, ] 
训练模型 
model <- rpart(Species ~ ., data = iris.train) 
测试模型 
results <- predict(object = model, newdata = iris.test, type = "class") 
table(results, iris.test$Species) 
results/ setosa versicolor virginica 
setosa 15 0 0 
versicolor 0 16 6 

virginica 0 1 12 


转自：http://blog.csdn.net/cmddds11235/article/details/47724871

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