There are two problems in the traditional spectral clustering algorithm. Firstly, when it uses Gaussian kernel function to construct the similarity matrix, different scale parameters in Gaussian kernel function will lead to different results of the algorithm. Secondly, K-means algorithm is often used in the clustering stage of the spectral clustering algorithm. It needs to initialize the cluster center randomly, which will result in the instability of the results. In this paper, an improved spectral clustering algorithm is proposed to solve these two problems. In constructing a similarity matrix, we proposed an improved Gaussian kernel function, which is based on the distance information of some nearest neighbors and can adaptively select scale parameters. In the clustering stage, beetle antennae search algorithm with damping factor is proposed to complete the clustering to overcome the problem of instability of the clustering results. In the experiment, we use four artificial data sets and seven UCI data sets to verify the performance of our algorithm. In addition, four images in BSDS500 image data sets are segmented in this paper, and the results show that our algorithm is better than other comparison algorithms in image segmentation.

中文翻译：

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基于改进的高斯核函数和带阻尼因子的甲壳虫天线搜索的谱聚类算法。

传统的频谱聚类算法存在两个问题。首先，当使用高斯核函数构造相似度矩阵时，高斯核函数中不同尺度参数会导致算法结果不同。其次，在频谱聚类算法的聚类阶段经常使用K-means算法。它需要随机初始化聚类中心，这将导致结果不稳定。本文提出了一种改进的频谱聚类算法来解决这两个问题。在构造相似矩阵时，我们提出了一种改进的高斯核函数，该函数基于一些最近邻的距离信息，并且可以自适应地选择尺度参数。在聚类阶段，提出了一种具有阻尼因子的甲虫天线搜索算法来完成聚类，克服聚类结果不稳定的问题。在实验中，我们使用四个人工数据集和七个UCI数据集来验证算法的性能。此外，本文对BSDS500图像数据集中的四幅图像进行了分割，结果表明我们的算法在图像分割方面比其他比较算法更好。