可伸缩约束聚类：一种广义谱方法
Scalable Constrained Clustering: A Generalized Spectral Method
摘要：
我们提出了一个简单的谱方法来研究约束聚类问题。它捕获约束聚类作为一个广义特征值问题与图拉普拉斯算子。该算法工作在近似线性的时间，并提供了具体的保证的质量的集群，至少在2路分区的情况下。在实践中，这转化为一个非常快的实现，始终优于现有的频谱方法在速度和质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
