Cobweb (3.7)

Learner

Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers

This algorithm always compares the best host, adding a new leaf, merging the two best hosts, and splitting the best host when considering where to place a new instance.

For more information see:

D.Fisher (1987).

Knowledge acquisition via incremental conceptual clustering.Machine Learning.

2(2):139-172.

J.H.

Gennari, P.Langley, D.

Fisher (1990).Models of incremental concept formation.

Artificial Intelligence.40:11-61.

(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.

Input Ports

  1. Type: Data Training data

Output Ports

  1. Type: Weka 3.7 Cluster Trained model

Find here

Analytics > Mining > Weka > Weka (3.7) > Cluster Algorithms

Make sure to have this extension installed:

KNIME Weka Data Mining Integration (3.7)

Update site for KNIME Analytics Platform 3.7:
KNIME Analytics Platform 3.7 Update Site

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