Correlation Clustering with Noisy Partial Information

Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan
; Proceedings of The 28th Conference on Learning Theory, PMLR 40:1321-1342, 2015.

Abstract

In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model. The first algorithm finds a solution of value (1+ δ)\mathrmopt-cost + O_δ(n\log^3 n) with high probability, where \mathrmopt-cost is the value of the optimal solution (for every δ> 0). The second algorithm finds the ground truth clustering with an arbitrarily small classification error η(under some additional assumptions on the instance).

Cite this Paper


BibTeX
@InProceedings{pmlr-v40-Makarychev15, title = {Correlation Clustering with Noisy Partial Information}, author = {Konstantin Makarychev and Yury Makarychev and Aravindan Vijayaraghavan}, booktitle = {Proceedings of The 28th Conference on Learning Theory}, pages = {1321--1342}, year = {2015}, editor = {Peter Grünwald and Elad Hazan and Satyen Kale}, volume = {40}, series = {Proceedings of Machine Learning Research}, address = {Paris, France}, month = {03--06 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v40/Makarychev15.pdf}, url = {http://proceedings.mlr.press/v40/Makarychev15.html}, abstract = {In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model. The first algorithm finds a solution of value (1+ δ)\mathrmopt-cost + O_δ(n\log^3 n) with high probability, where \mathrmopt-cost is the value of the optimal solution (for every δ> 0). The second algorithm finds the ground truth clustering with an arbitrarily small classification error η(under some additional assumptions on the instance).} }
Endnote
%0 Conference Paper %T Correlation Clustering with Noisy Partial Information %A Konstantin Makarychev %A Yury Makarychev %A Aravindan Vijayaraghavan %B Proceedings of The 28th Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2015 %E Peter Grünwald %E Elad Hazan %E Satyen Kale %F pmlr-v40-Makarychev15 %I PMLR %J Proceedings of Machine Learning Research %P 1321--1342 %U http://proceedings.mlr.press %V 40 %W PMLR %X In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model. The first algorithm finds a solution of value (1+ δ)\mathrmopt-cost + O_δ(n\log^3 n) with high probability, where \mathrmopt-cost is the value of the optimal solution (for every δ> 0). The second algorithm finds the ground truth clustering with an arbitrarily small classification error η(under some additional assumptions on the instance).
RIS
TY - CPAPER TI - Correlation Clustering with Noisy Partial Information AU - Konstantin Makarychev AU - Yury Makarychev AU - Aravindan Vijayaraghavan BT - Proceedings of The 28th Conference on Learning Theory PY - 2015/06/26 DA - 2015/06/26 ED - Peter Grünwald ED - Elad Hazan ED - Satyen Kale ID - pmlr-v40-Makarychev15 PB - PMLR SP - 1321 DP - PMLR EP - 1342 L1 - http://proceedings.mlr.press/v40/Makarychev15.pdf UR - http://proceedings.mlr.press/v40/Makarychev15.html AB - In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model. The first algorithm finds a solution of value (1+ δ)\mathrmopt-cost + O_δ(n\log^3 n) with high probability, where \mathrmopt-cost is the value of the optimal solution (for every δ> 0). The second algorithm finds the ground truth clustering with an arbitrarily small classification error η(under some additional assumptions on the instance). ER -
APA
Makarychev, K., Makarychev, Y. & Vijayaraghavan, A.. (2015). Correlation Clustering with Noisy Partial Information. Proceedings of The 28th Conference on Learning Theory, in PMLR 40:1321-1342

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