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

摘要： 在K-means型多视图聚类算法中，最终的聚类结果会受到初始类中心的影响。因此研究了不同的初始中心选择方法对K-means型多视图聚类算法的影响，并提出一种基于采样的主动式初始中心选择方法（sampled- clustering by fast search and find of density peaks，SDPC）。该方法通过对数据集进行均匀采样，利用密度峰值快速搜索聚类算法（clustering by fast search and find of density peaks，DPC），以及K-means再迭代策略，进一步改善多视图聚类中的初始中心选择效率和类个数问题。实验验证了不同初始化方法对K-means型多视图聚类算法的影响。多视图基准数据集上的实验结果表明：全局（核）K-means初始化方法存在时间复杂度过高的问题，AFKMC2（assumption-free K-Markov chain Monte Carlo）初始化适用于大规模数据，DPC可以主动选择类个数和初始类中心，SDPC较DPC而言,不仅能主动式获得类个数，还在聚类精度和效率上取得了较好的折衷。

关键词: 多视图, 类初始化, 聚类