sIB (3.7)

Learner

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.

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

How to install extensions