Efficiently Clustering Documents with Committees

@inproceedings{Pantel2002EfficientlyCD,
  title={Efficiently Clustering Documents with Committees},
  author={Patrick Pantel and Dekang Lin},
  booktitle={PRICAI},
  year={2002}
}
The general goal of clustering is to group data elements such that the intragroup similarities are high and the inter-group similarities are low. We present a clustering algorithm called CBC (Clustering By Committee) that is shown to produce higher quality clusters in document clustering tasks as compared to several well known clustering algorithms. It initially discovers a set of tight clusters (high intra-group similarity), called committees, that are well scattered in the similarity space… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
11 Citations
14 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

Similar Papers

Loading similar papers…