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arXiv · 1504.00653

Scalable Constrained Clustering: A Generalized Spectral Method

Mihai Cucuringu·Ioannis Koutis·Sanjay Chawla
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Abstract

We present a principled spectral approach to the well-studied constrained clustering problem. It reduces clustering to a generalized eigenvalue problem on Laplacians. The method 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. We support this claim with experiments on various data sets: our approach recovers correct clusters in examples where previous methods fail, and handles data sets with millions of data points - two orders of magnitude larger than before.

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Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla. 2015-04-02. Scalable Constrained Clustering: A Generalized Spectral Method. https://arxiv.org/abs/1504.00653

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