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Title: Correlation Clustering with Asymmetric Classification Errors
In the Correlation Clustering problem, we are given a weighted graph $G$ with its edges labelled as "similar" or "dissimilar" by a binary classifier. The goal is to produce a clustering that minimizes the weight of "disagreements": the sum of the weights of "similar" edges across clusters and "dissimilar" edges within clusters. We study the correlation clustering problem under the following assumption: Every "similar" edge $e$ has weight $w_e \in [ \alpha w, w ]$ and every "dissimilar" edge $e$ has weight $w_e \geq \alpha w$ (where $\alpha \leq 1$ and $w > 0$ is a scaling parameter). We give a $(3 + 2 \log_e (1/\alpha))$ approximation algorithm for this problem. This assumption captures well the scenario when classification errors are asymmetric. Additionally, we show an asymptotically matching Linear Programming integrality gap of $\Omega(\log 1/\alpha)$  more » « less
Award ID(s):
1718820 1934843
NSF-PAR ID:
10283262
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
International Conference on Machine Learning
Page Range / eLocation ID:
4641-4650
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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