Hierarchical confidence-based active clustering

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Nogueira, BM (Author) Other; Jorge, AM (Author) FCUP; Rezende, SO (Author) Other

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 216-217

27th Annual ACM Symposium on Applied Computing, SAC 2012

Trento, 26 March 2012 through 30 March 2012

Indexing

Scopus - 9 Citations

Other information

Authenticus ID: P-008-4RQ

DOI: 10.1145/2245276.2245318

Abstract (EN): In this paper, 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 concept of merge confidence in agglomerative clustering. The proposed method was compared with an un-supervised 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. © 2012 Authors.

Language: English

Type (Professor's evaluation): Scientific