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.
- Type: Data Training data
- Type: Weka 3.7 Cluster Trained model
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