Semi-Supervised Selective Affinity Propagation Ensemble Clustering With Active Constraints

• Main Authors: Qi Lei, Ting Li
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Semi-supervised learning; ensemble clustering; ensemble clustering selection; active learning strategy
• Online Access: https://ieeexplore.ieee.org/document/9024039/

The semi-supervised selective affinity propagation ensemble clustering with active constraints (SSAPEC) method combines affinity propagation (AP) clustering algorithm with ensemble clustering and incorporates prior knowledge (almost a pairwise constraint) to improve the clustering performance. In this paper, the evaluation criterion coverage ratio, which is based on the structure of clustering result, is employed to judge the homogeneity of the component clusters without the assistance of ground-truth during the component cluster selection. And to solve the problem of random selection of pairwise constraints, an active learning strategy is applied to find most informative constraints based on the ensemble clustering result; these pairwise constraints selected by active learning strategy lead to a similarity matrix adjustment. Then, the final result is obtained by running the Average-link algorithm on the adjusted similarity matrix. The experimental results on fourteen datasets demonstrate that the SSAPEC method proposed in this paper outperforms other state-of-the-art ensemble clustering schemes.