HCAC: Semi-supervised hierarchical clustering using confidence-based active learning

• 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: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 139-153

15th International Conference on Discovery Science, DS 2012

Lyon, 29 October 2012 through 31 October 2012

Indexing

Scopus - 5 Citations

Other information

Authenticus ID: P-008-726

DOI: 10.1007/978-3-642-33492-4_13

Abstract (EN): Despite their importance, hierarchical clustering has been little explored for semi-supervised algorithms. In this paper, we address the problem of semi-supervised hierarchical clustering by using an active learning solution with cluster-level constraints. This active learning approach is based on a new concept of merge confidence in agglomerative clustering. When there is low confidence in a cluster merge the user is queried and provides a cluster-level constraint. The proposed method is compared with an unsupervised algorithm (average-link) and two state-of-the-art semi-supervised algorithms (pairwise constraints and Constrained Complete-Link). Results show that our algorithm tends to be better than the two semi-supervised algorithms and can achieve a significant improvement when compared to the unsupervised algorithm. Our approach is particularly useful when the number of clusters is high which is the case in many real problems. © 2012 Springer-Verlag Berlin Heidelberg.

Language: English

Type (Professor's evaluation): Scientific