Synopsis

A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.

Note: at prediction time, a missing value is returned if no cluster is found for the instance.

The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (classificationViaClustering).

Options

The table below describes the options available for ClassificationViaClustering.

Option	Description
clusterer	The clusterer to be used.
debug	If set to true, classifier may output additional info to the console.

Capabilities

The table below describes the capabilites of ClassificationViaClustering.

Capability	Supported
Class	Nominal class, Binary class
Attributes	Unary attributes, Empty nominal attributes, Binary attributes, Missing values, Numeric attributes, Nominal attributes
Min # of instances	1

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