ClassificationViaClustering (3.7)

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.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


ClassificationViaClustering Options

D: If set, classifier is run in debug mode and may output additional info to the console

W: Full name of clusterer. (default: weka.clusterers.SimpleKMeans)

N: number of clusters. (default 2).

P: Initialize using the k-means++ method.

V: Display std. deviations for centroids.

M: Don't replace missing values with mean/mode.

A: Distance function to use. (default: weka.core.EuclideanDistance)

I: Maximum number of iterations.

O: Preserve order of instances.

fast: Enables faster distance calculations, using cut-off values. Disables the calculation/output of squared errors/distances.

num-slots: Number of execution slots. (default 1 - i.e. no parallelism)

S: Random number seed. (default 10)

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Missing values, Nominal class, Binary class] Dependencies: [] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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