Resource-Bounded Information Gathering for Correlation Clustering
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
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We present a new class of problems, called resource-bounded information gathering for correlation clustering. Our goal is to perform correlation clustering under circumstances in which accuracy may be improved by augmenting the given graph with additional information. This information is obtained by querying an external source under resource constraints. The problem is to develop the most effective query selection strategy to minimize some loss function on the resulting partitioning. We motivate the problem using an entity resolution task.
- Information Science
- Statistics and Probability