Information Bottleneck Co-clustering

  title={Information Bottleneck Co-clustering},
  author={Pu Patrick Wang and Carlotta Domeniconi},
Co-clustering has emerged as an important approach for mining contingency data matrices. We present a novel approach to co-clustering based on the Information Bottleneck principle, called Information Bottleneck Co-clustering (IBCC), which supports both soft-partition and hardpartition co-clusterings, and leverages an annealing-style strategy to bypass local optima. Existing co-clustering methods require the user to define the number of rowand column-clusters respectively. In practice, though… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.


Publications referenced by this paper.
Showing 1-10 of 10 references

Machine Learning

  • T. Mitchell
  • McGraw Hill
  • 1997
1 Excerpt

Direct Clustering of a Data Matrix

  • J. A. Hartigan
  • Journal of the American Statistical Association,
  • 1972
2 Excerpts

Similar Papers

Loading similar papers…