Unsupervised prediction

Key ideas

• Sometimes you don’t know the labels for prediction
• To build a predictor
• Create clusters
• Name clusters
• Build predictor for clusters
• In a new data set
• Predict clusters

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Iris example ignoring species labels

data(iris); library(ggplot2)
inTrain <- createDataPartition(y=iris$Species,
                              p=0.7, list=FALSE)
training <- iris[inTrain,]
testing <- iris[-inTrain,]
dim(training); dim(testing)

[1] 45  5

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Cluster with k-means

kMeans1 <- kmeans(subset(training,select=-c(Species)),centers=3)
training$clusters <- as.factor(kMeans1$cluster)
qplot(Petal.Width,Petal.Length,colour=clusters,data=training)

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Compare to real labels

table(kMeans1$cluster,training$Species)

   
    setosa versicolor virginica
  1      0          1        23
  2     35          0         0
  3      0         34        12

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Build predictor

modFit <- train(clusters ~.,data=subset(training,select=-c(Species)),method="rpart")
table(predict(modFit,training),training$Species)

   
    setosa versicolor virginica
  1      0          0        21
  2     35          0         0
  3      0         35        14

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Apply on test

testClusterPred <- predict(modFit,testing) 
table(testClusterPred ,testing$Species)

               
testClusterPred setosa versicolor virginica
              1      0          0        13
              2     15          0         0
              3      0         15         2

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Notes and further reading

• The cl_predict function in the clue package provides similar functionality
• Beware over-interpretation of clusters!
• This is one basic approach to recommendation engines
• Elements of statistical learning
• Introduction to statistical learning