Modified differential evolution algorithm for semi-supervised fuzzy clustering
改进微分进化算法的半监督模糊聚类

Through studying and classifying labeled and unlabeled data, this paper proposed a modified differential evolution algorithm for semi-supervised fuzzy clustering. Firstly, a small part of data was labeled from the whole dataset, and then these labeled data were used to guide the evolution process to partition unlabeled data. The modified algorithm introduces inertia-weighted coefficient by considering inertia-weighted idea of particle swarm algorithm, which keeps diversity of individual at early stages and quickens convergent speed at later stages, and at the same time improves the performance of the algorithm. The experimental results for remote sensing data indicate that the proposed approach can improve classification accuracy.