Latent Dirichlet Conditional Naive-Bayes Models for Privacy-Preservation Clustering

The paper introduces a model for privacy preservation clustering which can handle the problems of privacy preservation, distributed computing. First, the latent variables in latent Dirichlet conditional Naive-Bayes models (LDCNB)are redefined and some terminologies are defined. Second, Variational approximation inference for LD-CNBis stated in detail. Third, base on the variational approximation inference, we design a distributed EM algorithm for privacy preservation clustering. Finally, some datasets from UCI are chosen for experiment, Compared with the distributed k-means algorithm, the results show LD-CNB algorithm does work better and LD-CNB can work distributed,so LD-CNB can protect privacy information.