差分隐私HADPK-means++聚类算法OA

To address the issue of cluster center deviation caused by noise accumulation during iterations in differentially private k-means clustering,which severely compromises clustering utility,this paper proposes a highly available differential privacy HADPK-means++algorithm.The method enhances initial center quality through reverse-order-sorting-based initial cluster center selection,optimizes sample partitioning by introducing a new metric combining intra-cluster and inter-cluster similarity,and corrects the noisy cluster centers using the transformation invariance of differential privacy to prevent them from deviating from the valid data range.Experiments on multiple real-world datasets,including Iris and Wine,demonstrate that under the same privacy budget,the proposed algorithm outperforms existing mainstream differentially private k-means algorithms in terms of both F-measure and Normalized Mutual Information(NMI).The HADPK-means++algorithm effectively suppresses cluster center deviation,thereby improving clustering utility and robustness.