Cotraining Algorithm Based on Weighted Principal Component Analysis and Improved Density Peak Clustering

Abstract

In order to solve the problem of insufficient useful information of unlabeled samples added in the iterative process and the accumulation of classification errors caused by inconsistent labeling of samples by multiple classifiers, a cotraining algorithm based on weighted principal component analysis and improved density peak clustering is proposed. This paper firstly introduces the density peak clustering algorithm and the density peak clustering algorithm based on weighted voting consistency. In terms of experiments, the DPC-VM algorithm will be tested on the real datasets Seed, Haberman, and Vertebral, and the accuracy performance of the DPC-VM algorithm in clustering will be compared with the DPC algorithm. DPC-VM's dataset seed is 89.99, dataset Haberman is 55.69, and dataset Vertebral is 75.77. The dataset seed of DPC is 88.61, the dataset Haberman is 53.62, and the dataset Vertebral is 56.25. The dataset seed for E-FDPC is 40.38 and the dataset Haberman is 17.42. The dataset seed for K-means is 89.25 and the dataset Haberman is 51.36. The dataset seed for FCM is 89.49 and the dataset Haberman is 50.89. The performance of the DPC-VM algorithm on Acc is basically better than other algorithms.