A self-trained semi supervised fuzzy clustering based on label propagation with variable weights
Jiannan Zheng, Yuling Zhou, Tian Yong Deng, Xiyang Yang · 2017
Clustering accuracy of fuzzy clustering is sensitive to the structure of dataset to be studied. Semi-supervised clustering algorithms aim to increase the accuracy under the supervisions of a limited amount of labeled data, but the classification rate is highly dependent on the size of available labeled data. To overcome this disadvantage, we propose a novel semi-supervised clustering based on label propagation. Under our label propagation mechanism, an unlabeled datum propagates an estimated label from two aspects: (1) from its adjacent labeled data; (2) from a previous clustering result. The effects of these estimated labels are controlled by weights indicating their confidence levels. The effectiveness of the proposed model with label propagation scheme are evaluated by several real-life data sets. Experimental results show that accuracy level would increase by applying this learning scheme, compared to other semi-supervised models.