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 class 'kmeans' pmml( model, model_name = "KMeans_Model", app_name = "SoftwareAG PMML Generator", description = "KMeans cluster model", copyright = NULL, transforms = NULL, missing_value_replacement = NULL, algorithm_name = "KMeans: Hartigan and Wong", ... )
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. |
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. |
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.
Graham Williams
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.