Hierarchical confidence-based active clustering

Authors:
Bruno M. Nogueira;Alípio M. Jorge;Solange O. Rezende
Affiliations:
University of Sao Paulo, Sao Carlos, SP, Brazil;University of Porto, Portugal;University of Sao Paulo, Sao Carlos, SP, Brazil
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

Citing 3
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Abstract

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.