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Hierarchical confidence-based active clustering

Published: 26 March 2012 Publication History

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.

References

[1]
R. Gil-García and A. Pons-Porrata. Dynamic hierarchical algorithms for document clustering. Pattern Recognition Letters, 31(6): 469--477, 2010.
[2]
D. Klein, S. D. Kamvar, and C. D. Manning. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In ICML: Proc. of the 19th Intern. Conference on Machine Learning, pages 307--314, San Francisco, CA, USA, 2002. Morgan Kaufmann.
[3]
V.-V. Vu, N. Labroche, and B. Bouchon-Meunier. Boosting clustering by active constraint selection. In ECAI: Proc. of the 19th European Conference on Artificial Intelligence, pages 297--302, Amsterdam, Netherlands, 2010. IOS Press.

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Published In

cover image ACM Conferences
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
March 2012
2179 pages
ISBN:9781450308571
DOI:10.1145/2245276
  • Conference Chairs:
  • Sascha Ossowski,
  • Paola Lecca

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2012

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Author Tags

  1. active learning
  2. semi-supervised clustering

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  • Poster

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SAC 2012
Sponsor:
SAC 2012: ACM Symposium on Applied Computing
March 26 - 30, 2012
Trento, Italy

Acceptance Rates

SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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  • (2018)Detection of Leading Experts from ResearchGateInternational Journal of Business Analytics10.4018/IJBAN.20180701055:3(67-86)Online publication date: Jul-2018
  • (2018)Entropy-based active sparse subspace clusteringMultimedia Tools and Applications10.1007/s11042-018-5945-177:17(22281-22297)Online publication date: 1-Sep-2018
  • (2015)Robust active learning for the diagnosis of parasitesPattern Recognition10.1016/j.patcog.2015.05.02048:11(3572-3583)Online publication date: 1-Nov-2015
  • (2015)Active learning for semi-supervised clustering based on locally linear propagation reconstructionNeural Networks10.1016/j.neunet.2014.11.00663:C(170-184)Online publication date: 1-Mar-2015
  • (2014)An active learning paradigm based on a priori data reduction and organizationExpert Systems with Applications10.1016/j.eswa.2014.04.00741:14(6086-6097)Online publication date: Oct-2014
  • (2014)Group extraction from professional social network using a new semi-supervised hierarchical clusteringKnowledge and Information Systems10.1007/s10115-013-0634-x40:1(29-47)Online publication date: 1-Jul-2014
  • (2012)HCAC: Semi-supervised Hierarchical Clustering Using Confidence-Based Active LearningDiscovery Science10.1007/978-3-642-33492-4_13(139-153)Online publication date: 2012
  • (2012)Towards Quantitative Constraints Ranking in Data ClusteringDatabase and Expert Systems Applications10.1007/978-3-642-32597-7_11(121-128)Online publication date: 2012
  • (2012)SHACUNProceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects10.1007/978-3-642-31488-9_16(194-208)Online publication date: 13-Jul-2012

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