Finding Low Error Clusterings

@inproceedings{Balcan2009FindingLE,
  title={Finding Low Error Clusterings},
  author={Maria-Florina Balcan and Mark Braverman},
  booktitle={COLT},
  year={2009}
}
A common approach for solving clustering problems is to design algorithms to approximately optimize various objective functions (e.g., k-means or min-sum) defined in terms of some given pairwise distance or similarity information. However, in many learning motivated clustering applications there is some unknown target clustering; in such cases the pairwise information is merely based on some heuristics and the real goal is to achieve low error on the data. In these settings, an arbitrary… CONTINUE READING
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