pmml.kmeans: Generate the PMML representation for a kmeans object from the...

In pmml: Generate PMML for Various Models

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

Description

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

Usage

## S3 method for class '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.

Author(s)

Graham Williams

References

R project: K-Means Clustering

Examples

## Not run: 
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)

## End(Not run)

pmml documentation built on March 18, 2022, 5:49 p.m.