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A confidence-based active approach for semi-supervised hierarchical clustering
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nogueira, Bruno M. Jorge, Alípio Mário Rezende, Solange Oliveira |
| Copyright Year | 2011 |
| Abstract | Semi-supervised approaches have proven to be effective in clustering tasks. They allow user input, thus improving the quality of the clustering obtained, while maintaining a controllable level of user intervention. Despite being an important class of algorithms, hierarchical clustering has been little explored in semisupervised solutions. In this report, we address the problem of semi-supervised hierarchical clustering by using an active clustering solution with cluster-level constraints. This active learning approach is based on a new concept of merge confidence in an agglomerative clustering process. When there is lower confidence in a cluster merge the user can be queried and provide a clusterlevel constraint. The proposed method was compared with a unsupervised algorithm (average-link) and a semi-supervised algorithm based on pairwise constraints. The results show that our algorithm tends to be better than the pairwise constrained algorithm and can achieve a significant improvement when compared to the unsupervised algorithm. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.icmc.usp.br/CMS/Arquivos/arquivos_enviados/BIBLIOTECA_113_RT_369.pdf |
| Alternate Webpage(s) | http://conteudo.icmc.usp.br/CMS/Arquivos/arquivos_enviados/BIBLIOTECA_113_RT_369.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
