Active Selection Constraints for Semi-supervised Clustering Algorithms
Автор: Walid Atwa, Abdulwahab Ali Almazroi
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 6 Vol. 12, 2020 года.
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Semi.-supervised clustering algorithms aim to enhance the performance of clustering using the pairwise constraints. However, selecting these constraints randomly or improperly can minimize the performance of clustering in certain situations and with different applications. In this paper, we select the most informative constraints to improve semi-supervised clustering algorithms. We present an active selection of constraints, including active must.-link (AML) and active cannot.-link (ACL) constraints. Based on Radial-Bases Function, we compute lower-bound and upper-bound between data points to select the constraints that improve the performance. We test the proposed algorithm with the base-line methods and show that our proposed active pairwise constraints outperform other algorithms.
Active learning, semi-supervised clustering, pairwise constraints
Короткий адрес: https://sciup.org/15017472
IDR: 15017472 | DOI: 10.5815/ijitcs.2020.06.03
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