Towards a semi-supervised ensemble clustering framework with flexible weighting mechanism and constraints information

Jing Tang, Decheng Xu, Qingwei Cai, Shunlei Li, Amin Rezaeipanah

Abstract: Highlights•Presenting a robust semi-supervised method based on ensemble learning.•Configuration of an AHC-based clustering framework by joining ECM and SSC.•Introducing a new similarity measure based on a flexible weighting mechanism.•Development of a merit-based selection strategy to reduce complexity in consensus.•The proposed method performs well in sparse and large-scale data.