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

In pmml: Generate PMML for Various Models

Generate the PMML representation for a hclust object from the package amap.

Description

Generate the PMML representation for a hclust object from the package amap.

Usage

## S3 method for class 'hclust'
pmml(
  model,
  model_name = "HClust_Model",
  app_name = "SoftwareAG PMML Generator",
  description = "Hierarchical Cluster Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  centers,
  ...
)

Arguments

model	A hclust 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.
centers	A list of means to represent the clusters.
...	Further arguments passed to or from other methods.

Details

This function converts a hclust object created by the hclusterpar function from the amap package. A hclust object is a cluster model created hierarchically. The data is divided recursively until a criteria is met. This function then takes the final model and represents it as a standard k-means cluster model. This is possible since while the method of constructing the model is different, the final model can be represented in the same way.

To use this pmml function, therefore, one must pick the number of clusters desired and the coordinate values at those cluster centers. This can be done using the hclusterpar and centers.hclust functions from the amap and rattle packages respectively.

The hclust object will be approximated by k centroids and is converted into a PMML representation for kmeans clusters.

Value

PMML representation of the hclust object.

Author(s)

Graham Williams

References

R project: Hierarchical Clustering

Examples

## Not run: 

# Cluster the 4 numeric variables of the iris dataset.
library(amap)
library(rattle)

model <- hclusterpar(iris[, -5])

# Get the information about the cluster centers. The last
# parameter of the function used is the number of clusters
# desired.
centerInfo <- centers.hclust(iris[, -5], model, 3)

# Convert to pmml
model_pmml <- pmml(model, centers = centerInfo)

## End(Not run)

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