Active Semi-supervised Affinity Propagation Clustering Algorithm
LEI Qi1, YU Hui-Ping1, WU Min2
1.School of Information Science and Engineering, Central South University, Changsha 410083 2.School of Automation, China University of Geosciences, Wuhan 430074
Abstract Affinity propagation clustering algorithm is completely unsupervised and it has the problem of ignoring the internal structure and the hierarchy of clusters. An active learning and pair-wise constrains based semi-supervised affinity propagation (AP) clustering algorithm is proposed. An active learning strategy is designed and the data with the biggest uncertainty are queried to gain the valuable constraint information as much as possible. Based on the constraint information, the similarity matrix is adjusted to guide the clustering process in AP algorithm. To verify the effectiveness of the proposed algorithm, experiment is carried out on the UCI machine learning repository and face image datasets and the results indicate that the proposed clustering algorithm improves the clustering performance significantly.
2015, Vol. 28 
