sIB (3.6) (legacy)

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 3.6)

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

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

Input ports

1. Type: Table
   Training data
   Training data

Output ports

1. Type: Weka 3.6 Cluster
   Trained clusterer
   Trained clusterer