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sIB (3.7)

KNIME WEKA nodes (3.7) version 4.2.0.v202007031307 by KNIME AG, Zurich, Switzerland

Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported

sIB assign for each instance the cluster that have the minimum cost/distance to the instance.The trade-off beta is set to infinite so 1/beta is zero.

For more information, see:

Noam Slonim, Nir Friedman, Naftali Tishby: Unsupervised document classification using sequential information maximization.

In: Proceedings of the 25th International ACM SIGIR Conference on Research and Development in Information Retrieval, 129-136, 2002.

(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.

Options

sIB Options

I: maximum number of iterations (default 100).

M: minimum number of changes in a single iteration (default 0).

N: number of clusters. (default 2).

R: number of restarts. (default 5).

U: set not to normalize the data (default true).

V: set to output debug info (default false).

S: Random number seed. (default 1)

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: [Numeric attributes, No 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.

Input Ports

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Training data

Output Ports

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Trained model

Views

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.

Installation

To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:

KNIME 4.2

A zipped version of the software site can be downloaded here. Read our FAQs to get instructions about how to install nodes from a zipped update site.

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Developers

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