Generate the Predictive Model Markup Language (PMML)
representation for a KMeans object (cluster). 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="Rattle/PMML",
description="KMeans cluster model", copyright=NULL,
transforms=NULL, dataset=NULL,
algorithm.name="KMeans: Hartigan and Wong", \dots)
Arguments
model
a kmeans object.
model.name
a name to give to the model in the PMML.
app.name
the name of the application that generated the PMML.
description
a descriptive text for the header of the PMML.
copyright
the copyright notice for the model.
transforms
a coded list of transforms performed.
dataset
not used for kmeans.
algorithm.name
the variety of kmeans used.
...
further arguments passed to or from other methods.
Details
PMML is an XML based language which
provides a way for applications to define statistical and data mining
models and to share models between PMML compliant applications. More
information about PMML and the Data Mining Group can be found at
http://www.dmg.org.
The generated PMML can be imported into any PMML consuming
application, such as the Zementis ADAPA and UPPI scoring engines which allow for
predictive models built in R to be deployed and executed on site, in the cloud
(Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal, Sybase IQ, Teradata and
Teradata Aster) or Hadoop (Datameer and Hive).
References
Rattle home page: http://rattle.togaware.com
PMML home page: http://www.dmg.org
A. Guazzelli, W. Lin, T. Jena (2012), PMML in Action: Unleashing the Power
of Open Standards for Data Mining and Predictive Analytics. CreativeSpace
(Second Edition) - Available on Amazon.com - http://www.amazon.com/dp/1470003244.
A. Guazzelli, M. Zeller, W. Lin, G. Williams (2009), PMML: An Open Standard for
Sharing Models. The R journal, Volume 1/1, 60-65