Active constrained fuzzy clustering: A multiple kernels learning approach
作者：
Highlights：
• Active constrained clustering is examined in this paper.
• The proposed method relies on a multiple kernels learning setting.
• The method deals with linear inseparable, partially overlapping and noisy datasets.
• An active query selection heuristic was embedded into the clustering algorithm.
• The query selection heuristic is based on the measurement of mistake in clustering.
摘要
Highlights•Active constrained clustering is examined in this paper.•The proposed method relies on a multiple kernels learning setting.•The method deals with linear inseparable, partially overlapping and noisy datasets.•An active query selection heuristic was embedded into the clustering algorithm.•The query selection heuristic is based on the measurement of mistake in clustering.
论文关键词：Constrained clustering,c-Means fuzzy clustering,Multiple kernels,Active constraint selection
论文评审过程：Received 29 November 2013, Revised 16 August 2014, Accepted 10 September 2014, Available online 20 September 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.09.008
