基于凸差规划的不完整数据填充聚类

Incomplete Data Imputation Clustering Based on Difference of Convex Functions Programming

为了提升聚类性能,文中提出基于凸差规划(DCP)的不完整数据填充聚类算法.采用DCP对核模糊C均值目标进行凸差化改造,实现DCP聚类和数据缺失项填充的交替优化过程,从理论上证明交替优化的收敛性.在UCI数据集上的实验验证文中算法在缺失数据填充和聚类上的优势.

To improve the clustering performance, an incomplete data imputation clustering algorithm based on difference of convex functions programming （DCP） is proposed. DCP is applied to optimize the kernel-based fuzzy C-means objective function, and the alternative optimization process for DCP clustering and missing completion is given. The convergence of the ahernating optimization is proved theoretically. Experiments show the superiority of the proposed algorithm in missing completion and clustering performance.