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The rapid development of internet technology has brought convenience to people’s lives, increasingly making their work and personal activities inseparable from the internet. However, the various types of information data in the Internet are very large, and accurately obtaining useful information from them has become particularly important. Therefore, clustering analysis technology is widely applied in various fields. The elastic network algorithm is vulnerable to noise and other factors in clustering analysis, resulting in low accuracy of clustering results. To solve this problem, this study proposes an elastic network algorithm based on weighted properties for clustering analysis. After comparative experiments, the results showed that the accuracy of the proposed algorithm was 98%. The area under the precision recall curve was 0.86. The maximum F-value was 0.93. The area under the working characteristic curve of the subject was 0.94. The precision ultimately stabilized at 97%. All values were higher than the comparison algorithm. In the experiment, the running time of the algorithm was 11 seconds and the average error was 7%, both of which were lower than the comparison algorithm. When the algorithm was affected by Statistical dispersion and threshold parameters, the accuracy of the proposed algorithm was 71%, 69% and 67% respectively, which were better than the comparison algorithm. In conclusion, the proposed elastic network algorithm based on the weighted characteristics has high accuracy, strong stability and advantages in clustering.

Modifying and generating facial images with desired attributes are two important and highly related tasks in the field of computer vision. Some current methods can take advantage of their relationship and use a unified model to handle them simultaneously. However, producing high visual quality images on both tasks is still a challenge. To tackle this issue, we propose a novel model called adversarially regularized U-net (ARU-net)-based generative adversarial networks (ARU-GANs). The ARU-net is the major part of the ARU-GAN and is inspired by the design principle of U-net. It uses skip connections to pass different-level features from encoder to decoder, which preserves sufficient attribute-independent details for the modification task. Besides, this U-net-like architecture employs an adversarial regularization term to guide the distribution of latent representation to match the prior distribution, which guarantees to generate meaningful faces from this prior. We also propose a joint training technique for the ARU-GAN, which enables the facial attribute modification and generation tasks to learn together during training. We perform experiments on celebfaces attributes (CelebA) dataset and make visual analysis and quantitative evaluation on both tasks, which demonstrates that our model can successfully produce high visual quality facial images. Also, the results show that learning two tasks jointly can lead to performance improvement compared with learning them individually. At last, we further validate the effectiveness of our method by making an ablation study and experimenting on another dataset.