Name
Abstract | ||
|---|---|---|
Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation graph in such a way as to minimize the disagreements in the cluster assignments. In this paper, we discuss a relaxation to the original problem setting which allows probabilistic assignments of vertices to labels. By so doing, overlapping clusters can be captured. We also show that a known optimization heuristic can be applied to the problem formulation, but with the automatic selection of the number of classes. Additionally, we propose a simple way of building an ensemble of agreement functions sampled from a reproducing kernel Hilbert space, which allows to apply correlation clustering without the empirical estimation of pairwise correlation values. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39140-8_8 | SIMBAD |
Keywords | Field | DocType |
pairwise correlation value,correlation graph,cluster assignment,automatic selection,correlation clustering,stochastic labellings,crisp partition,problem formulation,original problem setting,empirical estimation,agreement function | k-medians clustering,Fuzzy clustering,Combinatorics,Clustering high-dimensional data,Correlation clustering,Algorithm,Constrained clustering,Cluster analysis,Reproducing kernel Hilbert space,Mathematics,Single-linkage clustering | Conference |
Citations | PageRank | References |
2 | 0.37 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Rebagliati | 1 | 34 | 3.30 |
| Samuel Rota Bulò | 2 | 564 | 33.69 |
| Marcello Pelillo | 3 | 1888 | 150.33 |