Name
Abstract | ||
|---|---|---|
In this paper we address the problem of active query selection for clustering with constraints. The objective is to determine automatically a set of user queries to define a set of must-link or cannot-link constraints. Some works on active constraint learning have already been proposed but they are mainly applied to K-Means like clustering algorithms which are known to be limited to spherical clusters, while we are interested in clusters of arbitrary sizes and shapes. The novelty of our approach relies on the use of a k-nearest neighbor graph to determine candidate constraints coupled with a new constraint utility function. Comparative experiments conducted on real datasets from machine learning repository show that our approach significantly improves the results of constraints based clustering algorithms. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/978-1-60750-606-5-297 | ECAI |
Keywords | Field | DocType |
active constraint selection,boosting clustering,repository show,active constraint learning,active query selection,comparative experiment,new constraint utility function,real datasets,candidate constraint,arbitrary size,clustering algorithm,k-nearest neighbor graph | Data mining,Fuzzy clustering,Cluster (physics),Correlation clustering,Computer science,Constraint learning,Constrained clustering,Boosting (machine learning),Artificial intelligence,Novelty,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
215 | 0922-6389 | 6 |
PageRank | References | Authors |
0.55 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viet-Vu Vu | 1 | 52 | 4.75 |
| Nicolas Labroche | 2 | 139 | 17.87 |
| Bernadette Bouchon-meunier | 3 | 1033 | 173.38 |