Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | JMLR Workshop and Conference Proceedings | Graph,Cluster (physics),Mathematical optimization,Correlation clustering,Computer science,Spectral approach,Eigendecomposition of a matrix,Constrained clustering,Spectral method,Scalability |
DocType | Volume | ISSN |
Conference | 51 | 1938-7288 |
Citations | PageRank | References |
6 | 0.40 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mihai Cucuringu | 1 | 146 | 17.52 |
| Ioannis Koutis | 2 | 555 | 29.58 |
| Sanjay Chawla | 3 | 1372 | 105.09 |
| Gary L. Miller | 4 | 3239 | 1155.26 |
| Richard Peng | 5 | 522 | 42.64 |