@inproceedings{3debeed11b1a484fbaf4b0492a465715,
title = "Simple and Scalable Constrained Clustering: A Generalized Spectral Method",
abstract = "We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem in which both matrices are 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.",
author = "Mihai Cucuringu and Ioannis Koutis and Sanjay Chawla and Gary Miller and Richard Peng",
year = "2016",
language = "English",
volume = "51",
series = "Jmlr Workshop And Conference Proceedings",
publisher = "Microtome Publishing",
pages = "445--454",
editor = "A Gretton and CC Robert",
booktitle = "Artificial Intelligence And Statistics, Vol 51",
address = "United States",
note = "19th International Conference on Artificial Intelligence and Statistics (AISTATS) ; Conference date: 09-05-2016 Through 11-05-2016",
}