classifyKamila {kamila}

Classify new data into existing KAMILA clusters

Description

A function that classifies a new data set into existing KAMILA clusters using the output object from the kamila function.

Usage

classifyKamila(obj, newData)

Arguments

obj

An output object from the kamila function.

newData

A list of length 2, with first element a data frame of continuous variables, and second element a data frame of categorical factors.

Details

A function that takes obj, the output from the kamila function, and newData, a list of length 2, where the first element is a data frame of continuous variables, and the second element is a data frame of categorical factors. Both data frames must have the same format as the original data used to construct the kamila clustering.

Value

An integer vector denoting cluster assignments of the new data points.

References

Foss A, Markatou M; kamila: Clustering Mixed-Type Data in R and Hadoop. Journal of Statistical Software, 83(13). 2018. doi: 10.18637/jss.v083.i13

Examples

# Generate toy data set
set.seed(1234)
dat1 <- genMixedData(400, nConVar = 2, nCatVar = 2, nCatLevels = 4,
  nConWithErr = 2, nCatWithErr = 2, popProportions = c(.5,.5),
  conErrLev = 0.2, catErrLev = 0.2)
# Partition the data into training/test set
trainingIds <- sample(nrow(dat1$conVars), size = 300, replace = FALSE)
catTrain <- data.frame(apply(dat1$catVars[trainingIds,], 2, factor), stringsAsFactors = TRUE)
conTrain <- data.frame(scale(dat1$conVars)[trainingIds,], stringsAsFactors = TRUE)
catTest <- data.frame(apply(dat1$catVars[-trainingIds,], 2, factor), stringsAsFactors = TRUE)
conTest <- data.frame(scale(dat1$conVars)[-trainingIds,], stringsAsFactors = TRUE)
# Run the kamila clustering procedure on the training set
kamilaObj <- kamila(conTrain, catTrain, numClust = 2, numInit = 10)
table(dat1$trueID[trainingIds], kamilaObj$finalMemb)
# Predict membership in the test data set
kamilaPred <- classifyKamila(kamilaObj, list(conTest, catTest))
table(dat1$trueID[-trainingIds], kamilaPred)