关键词: 多视图, 类初始化, 聚类

Abstract: In K-means-based multi-view clustering algorithms, the final clustering results will be affected by initial cluster centers. Therefore, this paper studies the effect of different initial center selection methods for K-means type multi-view clustering algorithms, and proposes an active initial center selection method SDPC (sampled-clustering by fast search and find of density peaks), which performs uniform sampling on the dataset, then uses DPC (clustering by fast search and find of density peaks), and adopts a K-means reiterative strategy so as to further improve the efficiency of selecting the number of clusters and initial centers in multi-view clustering. Experiments show the effect of different initialization methods on K-means type multi-view clustering algorithms. According to experimental results on multi-view benchmark datasets, the global (kernel) K-means initialization has a high time complexity, AFKMC2 (assumption-free K-Markov chain Monte Carlo) initialization is suitable for large-scale data, DPC can be used to actively select cluster numbers and initial centers, and SDPC can not only obtain initial cluster centers actively, but also make a good trade-off between clustering accuracy and efficiency.

Key words: multi-view, clustering initialization, clustering