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arXiv · 1601.04746

Scalable Constrained Clustering: A Generalized Spectral Method

Mihai Cucuringu·Ioannis Koutis·Sanjay Chawla·Gary Miller·Richard Peng
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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.

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Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary Miller, Richard Peng. 2016-01-18. Scalable Constrained Clustering: A Generalized Spectral Method. https://arxiv.org/abs/1601.04746

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