Simple and Scalable Constrained Clustering: a Generalized Spectral Method

Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary Miller, Richard Peng
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:445-454, 2016.

Abstract

We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacians. The algorithm works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outperforms existing spectral approaches both in speed and quality.

Cite this Paper

BibTeX
@InProceedings{pmlr-v51-cucuringu16, title = {Simple and Scalable Constrained Clustering: a Generalized Spectral Method}, author = {Cucuringu, Mihai and Koutis, Ioannis and Chawla, Sanjay and Miller, Gary and Peng, Richard}, booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics}, pages = {445--454}, year = {2016}, editor = {Gretton, Arthur and Robert, Christian C.}, volume = {51}, series = {Proceedings of Machine Learning Research}, address = {Cadiz, Spain}, month = {09--11 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v51/cucuringu16.pdf}, url = {https://proceedings.mlr.press/v51/cucuringu16.html}, abstract = {We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacians. The algorithm works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outperforms existing spectral approaches both in speed and quality.} }
Endnote
%0 Conference Paper %T Simple and Scalable Constrained Clustering: a Generalized Spectral Method %A Mihai Cucuringu %A Ioannis Koutis %A Sanjay Chawla %A Gary Miller %A Richard Peng %B Proceedings of the 19th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2016 %E Arthur Gretton %E Christian C. Robert %F pmlr-v51-cucuringu16 %I PMLR %P 445--454 %U https://proceedings.mlr.press/v51/cucuringu16.html %V 51 %X We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacians. The algorithm works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outperforms existing spectral approaches both in speed and quality.
RIS
TY - CPAPER TI - Simple and Scalable Constrained Clustering: a Generalized Spectral Method AU - Mihai Cucuringu AU - Ioannis Koutis AU - Sanjay Chawla AU - Gary Miller AU - Richard Peng BT - Proceedings of the 19th International Conference on Artificial Intelligence and Statistics DA - 2016/05/02 ED - Arthur Gretton ED - Christian C. Robert ID - pmlr-v51-cucuringu16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 51 SP - 445 EP - 454 L1 - http://proceedings.mlr.press/v51/cucuringu16.pdf UR - https://proceedings.mlr.press/v51/cucuringu16.html AB - We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacians. The algorithm works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outperforms existing spectral approaches both in speed and quality. ER -
APA
Cucuringu, M., Koutis, I., Chawla, S., Miller, G. & Peng, R.. (2016). Simple and Scalable Constrained Clustering: a Generalized Spectral Method. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 51:445-454 Available from https://proceedings.mlr.press/v51/cucuringu16.html.

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