Identifying Structure Across Pre-Partitioned Data
Abstract
We propose an information-theoretic clustering approach that incorporates a pre-known partition of the data, aiming to identify common clusters that cut across the given partition. In the standard clustering setting the formation of clusters is guided by a single source of feature information. The newly utilized pre-partition factor introduces an additional bias that counterbalances the impact of the features whenever they become correlated with this known partition. The resulting algorithmic framework was applied successfully to synthetic data, as well as to identifying text-based cross-religion correspondences.
Cite
Text
Marx et al. "Identifying Structure Across Pre-Partitioned Data." Neural Information Processing Systems, 2003.Markdown
[Marx et al. "Identifying Structure Across Pre-Partitioned Data." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/marx2003neurips-identifying/)BibTeX
@inproceedings{marx2003neurips-identifying,
title = {{Identifying Structure Across Pre-Partitioned Data}},
author = {Marx, Zvika and Dagan, Ido and Shamir, Eli},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {489-496},
url = {https://mlanthology.org/neurips/2003/marx2003neurips-identifying/}
}