Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties
作者：
Highlights：
• An approach to ensemble clustering based on weighted co-association matrices is theoretically substantiated.
• The upper bound for misclassification probability in attributing a pair of observations to clusters is found.
• It is proved that clustering quality is improved with an increase in ensemble size and the expected evaluation function.
• Analytical dependencies between ensemble size and the quality estimates are derived.
摘要
Highlights•An approach to ensemble clustering based on weighted co-association matrices is theoretically substantiated.•The upper bound for misclassification probability in attributing a pair of observations to clusters is found.•It is proved that clustering quality is improved with an increase in ensemble size and the expected evaluation function.•Analytical dependencies between ensemble size and the quality estimates are derived.
论文关键词：Weighted clustering ensemble,Co-association matrix,Latent variable model,Cluster validity index,Ensemble size,Error bound,Hyperspectral image segmentation
论文评审过程：Received 29 June 2016, Revised 20 September 2016, Accepted 15 October 2016, Available online 17 October 2016, Version of Record 28 October 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.10.017
