Generate the PMML representation for a kmeans object from the package stats.

The kmeans object (a cluster described by k centroids) is converted into a PMML representation.

# S3 method for kmeans
pmml(
  model,
  model_name = "KMeans_Model",
  app_name = "SoftwareAG PMML Generator",
  description = "KMeans cluster model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  algorithm_name = "KMeans: Hartigan and Wong",
  ...
)

Arguments

model

A kmeans object.

model_name

A name to be given to the PMML model.

app_name

The name of the application that generated the PMML.

description

A descriptive text for the Header element of the PMML.

copyright

The copyright notice for the model.

model_version

A string specifying the model version.

transforms

Data transformations.

missing_value_replacement

Value to be used as the 'missingValueReplacement' attribute for all MiningFields.

algorithm_name

The variety of kmeans used.

...

Further arguments passed to or from other methods.

Details

A kmeans object is obtained by applying the kmeans function from the stats package. This method typically requires the user to normalize all the variables; these operations can be done using transforms so that the normalization information is included in PMML.

References

R project: K-Means Clustering

Author

Graham Williams

Examples

if (FALSE) {
ds <- rbind(
  matrix(rnorm(100, sd = 0.3), ncol = 2),
  matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)
)
colnames(ds) <- c("Dimension1", "Dimension2")
cl <- kmeans(ds, 2)
cl_pmml <- pmml(cl)
}